Evidence-based medicine and the search for a science of clinical care
Notice bibliographique
Résumé
EVIDENCE-BASED MEDICINE AND THE SEARCH FOR A SCIENCE OF CLINICAL CARE Jeanne Daly Berkeley: University of California Press 2005, HB 276 pp, US 65.00 ISBN. 0 520 24316 1Jeanne Daly documents how evidence-based medicine turned from a subversive set of ideas of single-minded people across the globe, into the latest buzzword in clinical science. In contemporary health care, to be against evidence-based medicine is to be against science, progress, and rationality. Evidence-based medicine stands for practicing medicine with a scientific basis. Medical decision-making should be guided by the best available, carefully evaluated quantitative evidence rather than by the experiences of medical authorities. For supporters, evidence-based medicine is the answer to incompetent physicians, clinicians overwhelmed by an endless stream of research articles, lack of efficient interventions, practice variation, cost-overruns, lingering clinical uncertainties, stale medical education, and health care inequities. For critics, evidence-based medicine 'rips the heart out of medicine' to replace it with an algorithm. Evidence-based medicine, critics further argue, is insufficiently clinical, methodological myopic, and leads to mindless standardisation. Still, in one generation the critics have been put in a defensive position, forced to explain that with all the problems in health care why wouldn't better science improve results.Daly offers the history of evidence-based medicine and clinical epidemiology based on, what can be described methodologically as, intellectual network studies. The core of her book consists of in-depth interviews with leading evidence-based figures in Canada, United States, Britain and South Africa who comprise an international network of like-minded thinkers. She introduces a figure with a short characterisation (e.g. 'The Internationalist: Kerr White' 2005:40), and then sums in a couple of pages the person's career path and achievements based on their published record and interview data.So, how do you become a leading figure in evidence-based medicine? You are a male clinician trained in the sixties. You are deeply frustrated with what you consider wrong-footed medicine, and you question the authority of your teachers. You decide to discover the 'true science' behind medical interventions. You become a strong believer in the randomised clinical trial or in meta-analyses of clinical trials. You do some studies. Besides strong research credentials, you also have a knack for convincing your initially sceptical colleagues. Soon, you are spreading the evidence-based medicine gospel through education, databanks, textbooks, and institutes. You have become mainstream medicine. You ignore those who question your authority. All around the world, the career paths seem remarkably similar.Among the pioneers, the most effective advocate of evidence-based medicine is the charismatic David Sackett. At McMaster University in Canada, Oxford University in Britain, and in hundreds of talks, he links scientific evidence to the bedside. Sackett has a talent for inspiring colleagues to ignore established dogma, drawing in talents foreign to medicine, and building institutions. Sackett was inspired by Alvan Feinstein who had challenged medicine with a broad intellectual, research-based agenda to turn the clock back to a time of diagnostic taxonomies of clinical symptoms, relying on better medical technologies and quantitative principles. Another dominant figure was Archie Cochrane who made the case for randomised clinical trials as a way to eradicate bias in research studies. …
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».