Quality of Life at the End of Life: How Is the Solution Far Away?
Notice bibliographique
Résumé
Article Tools SPECIAL DEPARTMENTS Article Tools OPTIONS & TOOLS Export Citation Track Citation Add To Favorites Rights & Permissions COMPANION ARTICLES No companion articles ARTICLE CITATION DOI: 10.1200/JCO.2002.20.6.1704 Journal of Clinical Oncology - published online before print September 21, 2016 PMID: 11896122 Quality of Life at the End of Life: How Is the Solution Far Away? Davide TassinarixDavide TassinariSearch for articles by this author , Ilaria PanzinixIlaria PanziniSearch for articles by this author , Alberto RavaiolixAlberto RavaioliSearch for articles by this author , Marco MaltonixMarco MaltoniSearch for articles by this author , Sergio SartorixSergio SartoriSearch for articles by this author Marit S. JordhøyxMarit S. JordhøySearch for articles by this author , Stein KaasaxStein KaasaSearch for articles by this author , Jon Håvard LogexJon Håvard LogeSearch for articles by this author , Peter FayersxPeter FayersSearch for articles by this author Show More City Hospital, Rimini, ItalyPierantoni Hospital, Forlí, ItalyArcispedale S. Anna, Ferrara, ItalyNorwegian University of Science and Technology, Trondheim, Nordland Central Hospital, Bodø, NorwayUniversity Hospital of Trondheim, Norwegian University of Science and Technology, Trondheim, NorwayUniversity of Oslo, Oslo, Norwegian University of Science and Technology, Trondheim, NorwayNorwegian University of Science and Technology, Trondheim, Norway, University of Aberdeen, Aberdeen, United Kingdom https://doi.org/10.1200/JCO.2002.20.6.1704 First Page Full Text PDF Figures and Tables © 2002 by American Society of Clinical OncologyjcoJ Clin OncolJournal of Clinical OncologyJCO0732-183X1527-7755American Society of Clinical OncologyResponse15032002In Reply:We appreciate the comments of Tassinari et al to our article presenting the evaluation of a palliative care intervention using self-reported quality of life as an outcome measure.1 As pointed out in their letter, assessment of quality of life at the end of life evokes serious methodologic problems.Although our study was not designed to evaluate the very last weeks of life, the patients were followed from enrollment to death, and the compliance data present a good illustration of what others have also experienced—that is, as death is approached, compliance decreases to an unacceptable level. Attrition owing to functional decline seems, however, inevitable in a palliative care setting. So how can this problem be solved? Our data indicate that compliance to some extent might be improved by keeping the questions simple. Both the Impact of Event Scale and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire–C30 were used, and the proportion of missing items were substantially higher for the Impact of Event Scale, which comprises the most complicated questions and wordings.1,2 Simple methods such as the Edmonton Symptom Assessment Schedule have been found useful in clinical practice3; however, its validity in research may be questioned. For clinical trials, it is also essential to take present knowledge about compliance into account in sample size estimation. Increasing the sample size may help to provide sufficient power for quality of life analyses at the end of life, but it will not help to avoid bias caused by missing answers from the patients who are most seriously ill. At present there is no agreement on how to measure quality of life at the end of life, neither with regard to content of measures or methods of data collection. Although patient self-report is regarded the gold standard as underlined by Tassinari et al, there are indications that proxy ratings may be a reliable option.4 Maybe it is time to face reality and accept that such methods are necessary supplements in end-of-life care.Palliative care aims at improvement of the patient’s quality of life, focusing both on physical as well as social, psychologic, and spiritual aspects. To evaluate such services, various questionnaires have already been developed and used.5,6 Documented quality-of-life achievements are, however, scarce.6 As suggested by Tassinari et al, the lack of positive results might be related to the fact that there are no well-established and validated tools, and that those applied have not been suitable for the purpose. We agree that new outcome methodology is needed; however, based on our own trial and experience, we find that a discussion about how palliative care services should be evaluated is equally important. In our opinion, it is highly unlikely that all aspects of quality of life among heterogeneous groups of advanced cancer patients are influenced by broadly defined interventions. Continued efforts to document the overall effectiveness of palliative care using multidimensional quality-of-life measures seem fruitless. Gathering all evidence, there is no doubt that palliative care is worthwhile.7 Such services have been found to increase satisfaction and to change the use of health care services into more and less costly home care.7,8 Hence, we need to move on. To improve both existing and new services, we need to be more specific in targeting our research, both in terms of interventions and instruments and which patients to include in the studies. Instead of evaluating broadly, it is probably more appropriate to evaluate, for example, the effectiveness in treatment of pain, targeting patients with pain and using pain assessment tools for evaluation, likewise, if social or psychologic problems are the issue. New methodology should be developed accordingly, and preferably with a common metric9 based on international consensus. Only by such an approach can data be compared across studies to the best interest of all patients in palliative care.1. Jordhøy MS, Fayers P, Loge JH, et al: Quality of life in palliative cancer care: Results from a cluster randomized trial. J Clin Oncol 19:: 3884,2001-3894, Link, Google Scholar2. Jordhøy MS, Kaasa S, Fayers P, et al: Challenges in palliative care research; recruitment, attrition and compliance: Experience from a randomized controlled trial. Palliat Med 13:: 299,1999-310, Crossref, Medline, Google Scholar3. Bruera E, Kuehn N, Miller MJ, et al: The Edmonton Symptom Assessment System (ESAS): A simple method for the assessment of palliative care patients. J Palliat Care 7: (2): 6,1991-9, Crossref, Medline, Google Scholar4. Sneeuw KC, Aaronson NK, Sprangers MA, et al: Value of caregiver ratings in evaluating the quality of life of patients with cancer. J Clin Oncol 15:: 1206,1997-1217, Link, Google Scholar5. Hearn J, Higginson IJ: Outcome measures in palliative care for advanced cancer patients: A review. J Publ Health Med 19:: 193,1997-199, Crossref, Medline, Google Scholar6. Salisbury C, Bosanquet N, Wilkinson EK, et al: The impact of different models of specialist palliative care on patients’ quality of life: A systematic literature review. Palliat Med 13:: 3,1999-17, Crossref, Medline, Google Scholar7. Hearn J, Higginson I: Do specialist palliative care teams improve outcomes for cancer patients? A systematic literature review. Palliat Med 12:: 317,1998-332, Crossref, Medline, Google Scholar8. Ringdal GI, Jordhøy MS, Kaasa S: Family satisfaction with end of life care for cancer patients in a cluster randomized trial. J Pain Symptom Manage (in press) Google Scholar9. Ware JE, Bjorner JB, Kosinski M: Practical implications of item response theory and computerized adaptive testing: A brief summary of ongoing studies of widely used headache impact scales. Med Care 38:: II73,2000-II82, (suppl) Medline, Google Scholar
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,067 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,015 | 0,026 |
| Science ouverte | 0,004 | 0,009 |
| Intégrité de la recherche | 0,007 | 0,019 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,099 | 0,038 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».