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Enregistrement W2051886314 · doi:10.1200/jco.2008.18.7294

Progress in Quality-of-Care Research and Hope for Supportive Cancer Care

2008· article· en· W2051886314 sur OpenAlexaboutno aff
Karl Lorenz

Notice bibliographique

RevueJournal of Clinical Oncology · 2008
Typearticle
Langueen
DomaineMedicine
ThématiquePalliative Care and End-of-Life Issues
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPalliative careMedicinePsychosocialPsychological interventionContext (archaeology)Quality of life (healthcare)CancerNursingFamily medicineIntensive care medicinePsychiatry

Résumé

récupéré en direct d'OpenAlex

Supportive care encompasses the direct and treatment-related impacts of cancer, including the management of pain and other symptoms, and the psychosocial context of cancer, including spirituality and the challenges of caregiving. Patients and families endorse these concerns as critical aspects of health-related quality of life (HRQOL), and the need to provide excellent supportive care is relevant and important in all phases of cancer care. As earlier diagnosis and more effective treatments extend the experience of patients living with cancer or as disease-free survivors, seamless integration of supportive principles and approaches becomes even more imperative. Improving supportive cancer care is justifiably a priority, because cancer and its complications are associated with tremendous human suffering, because treatment typically holds the potential to harm as well as benefit, and because late-stage cancer in particular consumes enormous resources. Palliative care shares a focus on HRQOL, and is expanding rapidly in the United States and elsewhere. Palliative care is often hospital based and includes various service delivery models; however, limited evidence informs how such services, including hospice, can best serve cancer patients. Fortunately, the published literature includes a lot of evidence about what clinical interventions (eg, opioids for cancer pain), as opposed to service models (eg, opioids initiated by oncologists v palliative nurses) improve aspects of the HRQOL of cancer patients. Palliative care is often implemented late in patients’ care, but the clinical toolbox of palliative care may have much to offer patients and families with earlier-stage illness, and innovative programs integrate palliative services throughout the cancer chronology. Patients and families will be best served when oncology, palliative care, and the services that cancer patients need—including surgery and primary care—are fully integrated in various settings that cancer patients rely on. To illustrate using a recent example of an analysis of the outcomes of intensivist care from a large database, it is unfortunate that after decades of investment, fundamental questions persist as to how to best organize medical intensive care unit services. There is troubling evidence that many health care innovations are not sustained. Indeed, we might foster innovative programs and invest extensively in supportive care resources without improving HRQOL of the broad population of cancer patients and families. How can we abet the translation of relevant science into care improvement, promote programmatic development from the outset to best serve patients and families, and also ensure that supportive care improvements are sustained? An important and necessary solution to guide appropriate investment in supportive cancer services including palliative care is an accelerating focus on quality of care that transcends disciplinary boundaries. Quality of care has been defined as “the degree to which health services for individuals or populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.” In the case of supportive cancer care, that means that the focus of quality should be on helping patients achieve a higher HRQOL and helping providers deliver on processes of care (eg, prophylactic anti-emetics when the patient is at high risk for nausea and vomiting) that promote better HRQOL. In addition to outcomes or processes, quality can also focus on structure (eg, the presence of interdisciplinary providers in oncology clinics). If we take a societal view, we might consider limiting inappropriate care as an aspect of quality. Types of utilization may be harmful, and patients and families support the conceptual relationship between excess utilization and quality (eg, frequent late-life emergency department visits). This issue highlights the nascent capacity to identify which supportive issues are most important to patients and families, and which interventions will help them achieve a better HRQOL from the time of a cancer diagnosis. It also offers an emerging set of tools to evaluate the quality of supportive cancer care. Resource allocation and innovation should be driven by information about what current approaches are achieving results for patients and families, and concordance between our clinical practices and high-quality supportive care is likely to grow in importance to payors. We need to muster our collective will to ensure that developing tools reflect appropriate patientand familycentered concerns as well as the best clinical and professional input. These tools and other incentives must be appropriately tested, and they should become a focus for directing the resources to support continuous quality improvement. This issue features some important developments related to the quality of supportive cancer care in the United States, Canada, Australia, Europe, and Japan, and intentionally includes reports addressing supportive oncology care in general oncology and palliative settings. Recent efforts to improve supportive and palliative care, establish guidelines, and standardize practice are reviewed by Ferrell et al, who highlight some of the implications for practice, research, and professional education. American Society of Clinical Oncology’s Quality Oncology Practice Initiative (QOPI) focuses on developing and testing routinely JOURNAL OF CLINICAL ONCOLOGY O V E R V I E W VOLUME 26 NUMBER 23 AUGUST 1

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,065
score de la tête « metaresearch » (Gemma)0,138
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,065
Score d'incertitude au seuil0,342

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0650,138
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0040,008
Études des sciences et des technologies0,0030,010
Communication savante0,0150,017
Science ouverte0,0050,010
Intégrité de la recherche0,0070,014
Charge utile insuffisante (le modèle a refusé de juger)0,0180,003

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 Opus0,748
Tête enseignante GPT0,720
Écart entre enseignants0,028 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
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

Citations6
Publié2008
Routes d'admission1
Résumé présentoui

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