In Reply to Norman et al and to Ilgen et al
Notice bibliographique
Résumé
Norman et al’s response evades our point that their experimental conditions did not distinguish System 1 from System 2 processes. Again, in the time available to the res pondents in their study1 and its predecessor,2 it was quite possible to make an analytical response to a written clinical case containing the relevant data necessary to make a correct diagnosis. Therefore, speed of response alone did not distinguish individuals processing intuitively from those processing analytically. Any discussion of System 1 and System 2 in their papers is digressive, since there is no evidence that they are triggering System 1 processes in the cases they used. The variability in accuracy they observed could be explained by the extent of the respondent’s knowledge, and perhaps by their particular level of confidence. The information presented to respondents in these studies presumably had a high level of consistency, as all the data necessary to make a correct diag nosis would have to be present, and, when data consistency is high, diagnostic accuracy is associated with higher levels of certainty.3 They also state that “Errors in diagnosis are more likely to be rectified by con scientious acquisition of relevant knowledge (i.e. clinical experience) than by any attempt to extinguish general cognitive biases and thinking failures.” We agree; it is difficult to conceive of someone being a good diagnostician without knowledge and experience. However, it is misleading to present it as a choice. The question isn’t whether excellent content knowledge is better than de-biasing, or even whether System 2 is better than System 1. The question should be: Given the same content knowledge in the same real-world clinical scenario, does awareness of one’s potential biases and flawed decision-making habits, and use of reflective intelligence or appropriate “mindware”4 to make the necessary adjustments, improve performance? Finally, the statement that “the few studies directed at reducing error by explicating cognitive biases have been uniformly negative” is simply inaccurate. Graber et al’s review of 140 studies aimed at cognitive interventions to improve clinical reasoning and decision-making, which included reflective practice and active metacognitive review, found a number that showed positive, beneficial effects.5 To condemn the potential value of such work is premature. Ilgen et al misrepresent our position on “paper-based cases.” We were commenting on the unrepresentative nature of the conditions under which attempts were made to investigate clinical decision making (CDM).1,2 Written cases certainly have a role in the study of CDM but with the strong caveat that their use is as close as reasonably possible to the real clinical world. The study that we favourably commented upon6 was clearly in this category. Consideration of external and ecological validity is critically important in the interpretation of experimental CDM studies. Their comment that we might be biased ourselves is quite correct. It is widely appreciated that while scientists are ostensibly committed to the scientific principle of objectivity, they may ignore findings that contradict what they already believe—a relatively recent review of suggested educational strategies to promote clinical diagnostic reasoning omitted any discussion of cognitive bias.7 Bias is a normal operating characteristic of the human brain,8 and it is time to forego an ostrich-like attitude towards it. Their final point in which they challenge the value of reflection will be anathema to many who value the role of reflection in achieving quality of thought. We strongly recommend the broad perspective offered in Epstein’s classic paper,9 which soundly explores the value of reflection and mindfulness in clinical practice. Pat Croskerry, MD, PhD Professor and director, Critical Thinking Program, Division of Medical Education, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; e-mail: [email protected] David A. Petrie, MD Professor of emergency medicine and professor, Department of Emergency Medicine, Faculty of Medicine, Dalhousie University, and chief, Capital District Health Authority Department of Emergency Medicine, Halifax, Nova Scotia, Canada. James B. Reilly, MD, MS Associate director, Internal Medicine Residency, Allegheny General Hospital, Western Pennsylvania Hospital Educational Consortium, Pittsburgh, Pennsylvania, and assistant professor of medicine, Temple University School of Medicine, Philadelphia, Pennsylvania. Gordon Tait, PhD Assistant professor, Departments of Surgery and Anesthesia, and staff scientist, Department of Anesthesia, Toronto General Hospital, University Health Network, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
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 | 0,013 | 0,098 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,007 |
| Communication savante | 0,007 | 0,010 |
| Science ouverte | 0,007 | 0,004 |
| Intégrité de la recherche | 0,062 | 0,082 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,012 |
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 ».