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Enregistrement W4417023170 · doi:10.30770/2572-1852-111.3.49

Book Review

2025· article· en· W4417023170 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

aboutLe titre ou le résumé porte un signal canadien du lexique géographique.
no affAucune affiliation canadienne : ce travail est invisible pour une base fondée sur la seule affiliation.
Aucune affiliation canadienne. Une base fondée sur la seule affiliation (le devis habituel) n'aurait jamais vu ce travail. C'est l'un des travaux qui justifient l'inversion de la base.

Notice bibliographique

RevueJournal of Medical Regulation · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueClinical Reasoning and Diagnostic Skills
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEleganceEvent (particle physics)Intervention (counseling)Natural (archaeology)CommissionPower (physics)

Résumé

récupéré en direct d'OpenAlex

Random Acts of Medicine: The Hidden Forces that Sway Doctors, Impact Patients, and Shape our HealthAnupam B. Jena and Christopher Worsham Doubleday, 2023Chance, luck, and random events can alter the course of our lives. It turns out that chance also affects our medical care. Despite the best efforts of physicians to use data and science to determine the best course of treatment for their patients, random events—a child with an August birthday, the timing of a cardiology conference, or even a nearby marathon—can significantly impact health outcomes, often in unseen and surprising ways.This is the territory explored by Anupam B. Jena and Christopher Worsham in their fascinating book, Random Acts of Medicine: The Hidden Forces that Sway Doctors, Impact Patients, and Shape our Health. With colorful stories and cleverly designed natural experiments, the authors, both physicians and researchers, demonstrate how random but predictable occurrences affect our health, revealing profound insights on medicine. And, most importantly, they show how physicians and patients—and perhaps regulators—can use that information to improve medical care.Jena and Worsham demonstrate the power and elegance of the natural experiment, one that observes naturally occurring events without any intervention by the investigators, to uncover truths about our world. The authors’ findings ranged from the expected (mortality rates decrease during a Joint Commission inspection); to the mildly surprising (yes, it does matter if it is your surgeon's birthday); to the truly head-scratching (a patient who experiences a serious cardiac event when the top cardiologists are away at a conference has an increased chance of survival).Natural experiments also reveal that the effects of random events or circumstances can be compounded by the very human element of cognitive bias, which can further skew decision making. While physicians rely on cumulative experience and pattern recognition to do their job well, these heuristics can sometimes lead them astray. Jena and Worsham give examples of biases that may lead physicians down the wrong path, including the “left-digit bias” among cardiac surgeons, the “win-stay, lose-shift bias” that influences obstetricians, and the “anchoring bias” exhibited by pathologists.The authors describe de-biasing techniques that can help reduce the effect: medical students can be trained to be aware of cognitive bias, cognitive forcing strategies that encourage physicians to self-monitor decision making, simulations that allow physicians to make errors without exposing patients to harm, and risk calculators that use objective criteria to guide decision making.Recognizing the impact of these biases leads the authors to the question: What makes a good doctor? Are some doctors better than others? This topic, explored in depth in Chapter 9, is worth a look for anyone working on or with a state medical board.To answer these questions, Jena and Worsham conducted and reviewed studies addressing some rather controversial topics. Do female physicians have better outcomes than male physicians? Do doctors from top medical schools or who train at top hospitals perform better than their peers? How do international medical school graduates measure up against graduates of US and Canadian schools? Does a physician's race affect quality of care?The research that really caught my attention was on age: Are younger physicians better than older physicians? And here, I found myself examining my own bias: was I giving too much weight to the licensee's age when evaluating a case before my board?In a series of studies involving hospitalists, Jena and some colleagues examined Medicare data of 737,000 non-elective hospitalizations in 19,000 different hospitals from 2011-2013. They found that as doctors got older, their patients had higher mortality rates. Perhaps this was not surprising, as older doctors may be more prone to anchoring bias (they’ve seen this case a million times) and miss a tricky diagnosis, while younger doctors with more recent training are more up to date with clinical knowledge. But when the researchers took a deeper dive into the data and studied workload, the data revealed something unexpected: for older doctors with high case volumes, the difference in mortality rates disappeared. The conclusion: so long as the hospitalist sees a high volume of patients, the age of the doctor is irrelevant.Even more interesting, when researchers turned their attention to surgeons, they found that as the age of the surgeon with medium or high case volumes increased, patient mortality rate steadily dropped. The authors theorize that the experience and technical skill that older surgeons develop in the operating room may outweigh any advantages a younger surgeon may have.I will keep this in mind next time I choose my surgeon—and when I review a case before my board. Jena and Worsham's insightful work challenges long-held assumptions, presents a new way to look at medicine, and offers lessons to anyone interested in improving medicine and saving lives, including medical regulators.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,105
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Synthèse · Signal consensuel: aucune
Score de désaccord entre enseignants0,166
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,105
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,017
Tête enseignante GPT0,396
Écart entre enseignants0,378 · 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