HIP FRACTURE SURGERY: WHO SHOULD GO FIRST? A PERSONALIZED MEDICINE TOOL
Notice bibliographique
Résumé
In a resource limited environment, clinicians need to prioritize care. Identifying who would most benefit from care, specifically early care, could inform this decision. We introduce a new way to identify patients who will benefit the most when deciding who should be treated first, in hip fracture cases where timing of surgery matters. We assess the probability that the surgery timing is a necessary and sufficient cause for reduction of in-hospital mortality. This approach, Unit Selection based on counterfactual logic developed by Mueller and Pearl, provides a deeper understanding of individual benefits compared to traditional risk assessment tools. We studied hospital records of patient undergoing hip fracture in Canada over 8 years, using the CIHI Discharge Abstract Database. First, we compared the effect of having surgery within two days to waiting longer on 64 groups (strata) of patients with different health, age, hospital, and care factors. Using a Unit Selection approach, we estimated the probability of benefit (decreased probability of mortality), or how likely each group was to benefit from early surgery. We measured the benefit for each person by comparing their potential outcomes after early and delayed surgery. In a cohort of 139,119 patients (74.3% women, 45.8% 85 years or older, 67% receiving early surgery -within 2 days), the average effect showed 8 fewer deaths per 1,000 surgeries when treatment was received early, within 2 days. In 14 out of 64 groups there was a much greater benefit from early surgery than the stated average: with their upper bound ranging from 7% to 15%. We identified Pre-hospital place of residence, Age, Type of Surgery (arthroplasty vs fixation) and Care environment (teaching vs community hospital), as important factors that define the population that may benefit from early surgery (Fig 1) Measuring probability of benefit using the Unit Selection method helped identify “who should go first” by looking at how likely it is that an individual patient benefit from early surgery. We created a Personalized Decision Making Tool that compares the individual-level benefit in different groups based on their clinical and care factors. This could assist doctors in determining which patients should receive hip fracture surgery first when prioritization is necessary. For any figures or tables, please contact the authors directly.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| 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) | 0,002 | 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 ».