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Enregistrement W2327164536 · doi:10.1097/01.cot.0000291850.57208.0a

Putting Quality-of-Life Measures into Practice

2004· article· en· W2327164536 sur OpenAlexaboutno aff
Rabiya S. Tuma

Notice bibliographique

RevueOncology Times · 2004
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealth Systems, Economic Evaluations, Quality of Life
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPsychosocialQuality of life (healthcare)PublishingFamily medicineMedicinePsychologyAlternative medicineGerontologyMedical educationNursingPsychiatryPolitical science

Résumé

récupéré en direct d'OpenAlex

ORLANDO, FL—Psychosocial researchers, and much of the cancer community at large, appreciate at a conceptual level what quality-of-life (QoL) studies can reveal about how patients are coping with their disease and treatment. But even with such research in place, it isn't yet obvious to clinicians how to incorporate such data into their practice. So says Jeff Sloan, PhD, Chair of the Quality-of-Life Research Committee for the Center of Cancer Statistics Division of Health Sciences Research at the Mayo Clinic. To help clinicians incorporate QoL data into their practice, Dr. Sloan and an informal group of about 30 other QoL researchers, who call themselves the Clinical Significance Consensus Meeting Group, started out in 2002 by publishing six papers in the Mayo Clinic Proceedings (April, May, and June issues). The papers addressed methodological issues around quality-of-life measurements. “People said the papers were good in theory, but that we were still talking a little too isometrically for them. They asked us if we could translate that into information they could use in the clinic,” Dr. Sloan explained in an interview. The Group then convened a meeting last fall in Phoenix, where they outlined five additional papers that will illustrate ways clinicians can use QoL data. On behalf of his colleagues, Dr. Sloan presented abbreviated outlines for each of the papers at the first annual meeting of the American Psychosocial Oncology Society, held here earlier this year. The expectation is to submit the papers for publication later this year. What QoL Data Add to a Clinical Interview In the first, Gordon Guyatt, MD, MSc, Professor of Clinical Epidemiology and Biostatistics at McMaster University in Hamilton, Ontario, and six colleagues, demonstrate what QoL data add to a clinical interview based on a case report. The volunteer for the case study was a 67-year-old woman actively involved in advocacy groups at the Mayo Clinic. She was diagnosed with early-stage renal cell carcinoma five years ago and breast cancer, three years ago. There is a recent question of whether the renal cell cancer is recurring, although the clinicians are currently taking a watchful-waiting approach. Although the patient has a history of multiple health problems in addition to the cancer, she is upbeat and active, and would describe herself as an “engine that could” sort of person, Dr. Sloan said. As part of the case study, she filled out a series of QoL forms, including the Function Assessment of Cancer Therapy-General (FACT-G), the Linear Analog Scale Assessment (LASA), and the Profile of Mood States-Short Form (POMS-SF). Her scores were then analyzed and interpreted individually by a nurse, an oncologist, a statistician, a clinical researcher, and a psychologist. The clinicians then fed their interpretations back to her, and she told them what they got right and what they missed.Figure: Jeff Sloan, PhD: “We know that there are problems with tumor measurement, but we allow for them so that we can get the job done. That is where we need to go with quality-of-life data. There are enough measures out there that are sufficiently reliable that we should be able to, with confidence, communicate to our clinical colleagues that this is not soft science and is based on sound measurement principles.”It turns out that although she appears outwardly to be coping well, she had low scores in the areas of pain and physical well-being and on some social issues. Dr. Sloan said that he was surprised by these results, even though he was personally familiar with her. However, when the researchers explored the issues with her further, guided by her responses on the forms, they found that she was experiencing significant pain, which was interfering with her sleep. Also, peripheral neuropathy, which caused her to use a cane, was limiting her mobility. When questioned about her mood, she admitted that her chipper exterior was a coping strategy and that she was actually quite distressed about the watchful waiting. “The point of all this was that a very simple exercise of getting these QoL scores did enrich the clinician-patient dialogue,” Dr. Sloan reported. “The value added [to the consultation] was that more things were found out, specific issues that would not have been found out otherwise.” The example demonstrates that QoL measures can be an efficient way of gathering data about a patient's functioning and well-being. “It was done in an efficient manner. It didn't take her long. It didn't take us long to assess them, and we presented the results to her and to her clinicians in a very straightforward way,” he said. Such measures can also alert clinicians to problems that they otherwise wouldn't have seen, such as the coping problems this woman was having. After learning more about how she was feeling, the team talked to her and her clinicians so that they could make appropriate changes in their approach. Finding Good Examples in the Literature The second paper demonstrates how clinicians can evaluate studies in the literature and how such data can be used to guide their clinical practice. David Osoba, MD, FRCPC, from QOL Consulting in West Vancouver, British Columbia, is the lead author on this paper. Dr. Osoba's group first set out criteria that clinicians can use to evaluate papers in the literature. This information is designed to help a doctor identify which studies are of high enough quality to use as guides and which ones aren't quite up to that level. In the second part of the manuscript, the authors review seven papers that they identified in the literature that meet these quality standards and that had a significant impact on practice. For example, two of the papers deal with a randomized controlled trial in which 161 men with metastatic hormone-resistant prostate cancer were treated with either prednisone alone or prednisone plus mitoxantrone. According to quality-of-life scores, which were measured at both baseline and at three weeks with two instruments, the patients on the combination therapy showed a greater improvement in pain, role functioning, fatigue and several other QoL outcomes. Although there was no survival advantage with the combination therapy, the FDA licensed the combination therapy for palliative treatment in this patient population based on this study, Dr. Sloan noted. The group's new manuscript concludes that although there are a lot of bad studies out there, there are also a lot of good ones. More recent studies tend to be better, Dr. Sloan said, because the psychosocial research community has developed better methodology with experience. Measurement Error In the third paper, the group looked at the measurement error in QoL data, relative to the errors involved in other clinical measurements. David Cella, PhD, Director of the Center on Outcomes Research and Education at Evanston Northwestern Healthcare and Co-leader of the Cancer Control Program at the Robert H. Lurie Comprehensive Cancer Center of Northwestern University, is the lead author. “Dave and I both have this as a pet peeve that there is measurement error inherent in tumor response, but we don't talk about that,” Dr. Sloan said. “We accept that.” Yet, the measurement error in QoL data is talked about frequently. To determine if that skeptical view of QoL data was justified, the team reviewed the literature and looked at the precision of QoL tools relative to other clinical measures. In terms of measures used frequently in oncology, they found that hemoglobin and survival both have high reliability. But Dr. Cella's group also found that the FACT-Fatigue scale is highly reliable. The FACT-G scale was only moderately reliable, in comparison. Significantly, tumor measurement over time showed low reliability, especially when measured by different observers, and yet this is, as Dr. Sloan points out, accepted by the community. “We know that there are problems with tumor measurement, but we allow for them so that we can get the job done. That is where we need to go with QoL. There are enough measures out there that are sufficiently reliable that we should be able to, with confidence, communicate to our clinical colleagues that this is not soft science and is based on sound measurement principles.” Cost of Incorporating QoL Data into Practice Dr. Sloan and several colleagues then looked at the cost of incorporating QoL measurements into clinical practice or into research. Although it is impossible to get numbers for everything involved, the cost estimates appear to vary considerably depending on whether or not the resources used are viewed as being a part of the infrastructure or required as an additional resource. When the team compared the cost of collecting QoL information with other outcome measures, they found that QoL assessments were approximately like collecting biological or clinical assessment data. The QoL data were less expensive than either genetic biomarker data collection or tumor response information. Timing, Referrals, Therapy Changes In the final paper in the group, a group led by Marlene H. Frost, RN, PhD, also of the Mayo Clinic, assessed the literature to find out how people are currently incorporating QoL measures into their clinics in a formal manner. They found that on average adding a QoL assessment added five or fewer minutes to a consultation. However, it did increase the number of referrals and elicited changes in therapy. They found different examples of how QoL measurements are being systematically incorporated into clinical settings. In one case, a clinic is using the Short-Form 36 (SF-36) to get a profile of the patient's quality of life in an automated way as someone comes through the door. In another example, providers give the Dartmouth COOP charts to patients as they come in the door and have them fill them out on their own. In summing up, Dr. Sloan said, “QoL is new. Pain, 25 years ago, wasn't thought to be measurable except by physicians. Now it is done on intake. We have come a long way. There are problems with QoL, but those things have gotten too much press in the literature. There are answers for everything.” “What we are trying to do is put some information out to show some solutions, and say ‘Here is how you can do it,’ so that clinicians can get the answers they have been asking for, to the question of ‘I know this is important, but how do I do it?’” New York State Attempts to Legislate Pain Management for Patients New York State has joined several others in an attempt to legislate pain management for patients. In early March, S 6312, a pain management bill, was introduced in the Senate. A few weeks later, Health Committee Chair Richard N. Gottfried (D-Manhattan) introduced the bill into the State Assembly. It is modeled on laws passed in other states and is championed by the American Cancer Society, New York State Hospice and Palliative Care Association, and the Compassion in Dying Federation. “This comprehensive legislation will make dramatic improvements in pain management for seriously ill and dying patients, said Kathryn L. Tucker, Legal Director, Compassion in Dying Federation.” “It addresses the two major causes of undertreatment of pain: inadequate physician education and physicians' concern that prescribing strong pain medication will bring scrutiny and sanction.” Provisions The bill will: ▪ Provide protection from discipline and liability for health care practitioners when prescribing pain medication within standards of care. ▪ Specify actions that can result in professional discipline or criminal prosecution. ▪ Establish continuing education requirements about pain management for physicians and other health care providers. ▪ Order hospitals to confirm these requirements when granting admitting privileges. ▪ Establish that failure to adequately manage patient pain is grounds for medical discipline for physicians and other providers. “Many health care professionals are unaware of the new resources available for their patients to relieve acute and chronic pain, and many are unwilling to use those resources because they fear they will be wrongly subjected to prosecution or professional discipline,” said Assemblyman Gottfried. “This bill is designed to change that.”

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,027
score de la tête « metaresearch » (Gemma)0,035
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,755
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0270,035
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,417
Tête enseignante GPT0,506
Écart entre enseignants0,089 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2004
Routes d'admission1
Résumé présentoui

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