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Enregistrement W2411943699 · doi:10.1097/prs.0b013e31825dc435

Discussion

2012· letter· en· W2411943699 sur OpenAlexaffabout
Andrea L. Pusic, Anne F. Klassen, Stefan Cano

Notice bibliographique

RevuePlastic & Reconstructive Surgery · 2012
Typeletter
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealth Systems, Economic Evaluations, Quality of Life
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésTerminologyQuality (philosophy)Outcome (game theory)PsychologyInterpretation (philosophy)Computer scienceMedical educationMedicineEpistemology

Résumé

récupéré en direct d'OpenAlex

Sir:FigureAlthough we appreciate Dr. Hammond's commentary1 on our article,2 we would like to take the opportunity to respond to and clarify four of the key issues raised. “Overall, the article is highly technical and uses detailed statistical and study design terminology that is difficult to fully understand.” Over the past decade, our team has conducted and published primary research, reviews, and education pieces in the area of patient-reported outcome instruments. Research related to the BREAST-Q is an example of our work.2–5 Our motivation is simple: we feel it is essential that plastic surgeons play a central role in the development and application of patient-reported outcome instruments, especially at a time when interpretation of such data is becoming so vital to quality care.6 It is important that practicing surgeons be exposed to the science of psychometric research, and with this in mind, we respectfully make no apologies for the technical nature of our article. Questionnaire development and validation is inherently complex work. To develop and validate high-quality patient-reported outcome instruments, robust data from large heterogeneous patient cohorts are analyzed using state-of-the-art psychometric methods. Such methods make it possible to distinguish good items from bad and to ensure that the final scales provide reliable, valid, and responsive measurement. Like the iPhone, the BREAST-Q may be simple for people to use, but the underlying design is necessarily intricate and technical. Although our team strove to make the methods in our article as easy to understand and transparent as possible, it behooves the plastic surgery community to become knowledgeable about these research methods. Just as plastic surgeons learn new and complex surgical techniques, they should also be prepared to learn new techniques and terminology in clinical research. As a potentially useful starting point, we would refer Dr. Hammond to our article entitled “The Science behind Quality-of-Life Measurement: A Primer for Plastic Surgeons.”7 “It is possible that one unfortunate byproduct of using the BREAST-Q may well be to actually stifle scientific inquiry.” Helmholtz's famous dictum “all science is measurement” was ably countered by Kelvin's “all science is measurement, but not all measurement is science.” This is no more true than for the human sciences8 and especially in health measurement.9 However, in the same way as Helmholtz was committed to the creation of and use of high-quality experimental data, we (as patient-reported outcome instrument developers) constantly strive to exceed the highest scientific standards to drive the quality of heath measurement in plastic surgery. We hope that our growing body of work in plastic surgery will provide an ever improving evidence base for rigorous patient-reported outcome data collection. Therefore, given the intent and the rigor of research, we find it difficult to imagine a scenario in which the BREAST-Q might actually stifle scientific inquiry. Although scientific inquiry begins with creative ideas and questions, the ultimate aim should be to move researchable ideas into rigorously designed studies. Our team took an idea and, over 5 years of research, developed a patient-reported outcome instrument, which is now available for use by anyone in the plastic surgery community. The BREAST-Q is one of an increasing number of patient-reported outcome tools available to facilitate (not stifle) scientific inquiry in the specialty of plastic surgery. As the measurement of patient-reported outcomes has become an integral component of clinical research and quality improvement efforts in most other specialties, we would encourage the plastic surgery research community to consider including patient-reported outcomes in new studies being designed. However, not all patient-reported outcome instruments are created equal and thus, as we note above, it is essential that plastic surgeons be able to discern what actually makes a quality metric. “It is not unreasonable that a researcher with a specific bias could manipulate the application of the instrument in a manner such that a particular bias is supported.” Bias is an inherent risk in the design of any study, and it is unfortunate that some researchers may manipulate study results. The BREAST-Q is a scientifically credible and clinically meaningful tool designed to help minimize bias. Just like any measurement tool, however, the BREAST-Q will not be able to redeem a poorly designed or badly conducted study. “It remains unclear what the financial implications of the copyright are as it pertains to scientific inquiry.” The BREAST-Q is copyrighted to protect it from modifications by individual users. Any changes to the items or scales would affect the measurement properties of the scales, interfere with accurate raw data scoring, compromise the quality of studies performed using the BREAST-Q, and limit comparability between studies. There is no royalty fee for academic researchers or clinicians who wish to use the BREAST-Q. Andrea L. Pusic, M.D., M.H.S. Memorial Sloan-Kettering Cancer Center, New York, N.Y. Anne F. Klassen, D.Phil. McMaster University, Hamilton, Ontario, Canada Stefan J. Cano, Ph.D. Peninsula College of Medicine and Dentistry, Plymouth, United Kingdom DISCLOSURE Dr. Pusic is a codeveloper of the BREAST-Q, a patient-reported outcome measure owned by Memorial Sloan-Kettering Cancer Center and the University of British Columbia. Based on the inventor-sharing policies of these institutions, Dr. Pusic receives of portion of royalty generated by the use of the measure in industry-sponsored clinical trials.

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)0,003
score de la tête « metaresearch » (Gemma)0,020
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: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,212
Score d'incertitude au seuil0,000

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,020
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,001
Communication savante0,0040,004
Science ouverte0,0020,004
Intégrité de la recherche0,0060,007
Charge utile insuffisante (le modèle a refusé de juger)0,2120,084

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 Opus0,254
Tête enseignante GPT0,360
Écart entre enseignants0,106 · 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
GenreCommentaire

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
Publié2012
Routes d'admission2
Résumé présentoui

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