Back to the future: medical reversals and perioperative medicine
Notice bibliographique
Résumé
While most of us were enjoying the pleasures of an idle summer, a publication in Mayo Clinic Proceedings created a stir in the esoteric world of evidence-based medicine. The subject of interest was medical reversal, a phenomenon in which ‘‘a medical practice is found to be inferior to some lesser or prior standard of care’’. Prasad et al. reviewed original articles published in the New England Journal of Medicine from 2001 to 2010 in their endeavour to determine if new evidence advanced, confirmed, or rejected current medical practices. Seven hundred fiftysix (56%) of the 1,344 manuscripts reviewed found a new therapy superior to an older treatment, 138 (10%) confirmed the utility of a current therapy, and 165 (12%) found new practices inferior to present care. One hundred forty-six (40%) of the 363 studies evaluating an existing medical practice found the current therapy inferior to a lesser or earlier standard of care. These rejections of current practice for an older treatment (or no treatment at all) cut across classes of medical care, including anesthesia. Cited examples from perioperative medicine included bispectral index monitoring, mild hypothermia for an intracranial aneurysm clipping, use of a pulmonary artery catheter for high-risk surgical patients, coronary revascularization before elective vascular surgery, epidurals in early labour, and use of aprotinin in cardiac surgery. The reaction to Prasad’s findings was swift and critical. An accompanying editorial stated, ‘‘[T]he proportion of medical reversals seems alarmingly high. At a minimum, it poses major questions about the validity and clinical utility of a sizeable portion of everyday medical care.’’ The blog, Science-Based Medicine, noted, ‘‘[T]his highlights the fact that some current practices are useless or less than optimal and need to be reexamined.’’ Even the New York Times got in on the action by leading with the headline, ‘‘Medical Procedures May Be Useless, or Worse.’’ Both the evidence base for medical practice and the practice itself were under attack. Patients and clinicians alike could be left wondering how a supposedly science-based practice could have been wrong so frequently. Prasad identified a common narrative among the reversals noting that, ‘‘[a]lthough there is a weak evidence base for some practice, it gains acceptance largely through vocal support from prominent advocates and faith that the mechanism of action is sound. Later, future trials undermine the therapy, but removing the contradicted practice often proves challenging.’’ Does this sound familiar? Let us consider the saga of perioperative beta-blockade from Prasad’s perspective of medical reversal. In 1996, Mangano published results of a 200-patient placebo-controlled trial evaluating a seven-day perioperative course of atenolol on a composite outcome of cardiac mortality and morbidity. Six patients who died in hospital were excluded. Of those surviving to hospital discharge, 12 of 99 placebo patients died within six months of surgery compared with four of 95 patients receiving atenolol (crude relative risk [RR] 0.35; 95% confidence interval [CI] 0.1 to 1.0). Several years later, Poldermans G. L. Bryson, MD (&) Department of Anesthesiology, The Ottawa Hospital, The University of Ottawa, 1053 Carling Avenue, Box 249C, Ottawa, ON K1Y 4E9, Canada e-mail: glbryson@ottawahospital.on.ca
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 | 0,019 | 0,074 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,010 |
| Communication savante | 0,012 | 0,013 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,020 | 0,040 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,003 |
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.
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 ».