What Are the Tradeoffs in Outcomes after Casting Versus Surgery for Closed Extraarticular Distal Radius Fractures in Older Patients? A Statistical Learning Model
Notice bibliographique
Résumé
BACKGROUND: Distal radius fractures (DRFs) are one of the most common major fractures. Despite their frequency, the tradeoffs in different outcomes after casting or surgery for closed extraarticular DRFs in older adults are unknown. QUESTIONS/PURPOSES: (1) For adults older than 60 years with closed extraarticular DRFs, what are the tradeoffs in outcomes for choosing casting versus surgery? (2) In what settings would surgery be preferred over casting? METHOD: This is a secondary analysis of data from the Wrist and Radius Injury Surgical Trial (WRIST), a randomized, multicenter clinical trial that enrolled patients from April 10, 2012 to December 31, 2016. For WRIST, researchers recruited patients older than 60 years who sustained closed extraarticular distal radius fractures from 24 sites in the United States, Canada, and Singapore. We conducted a secondary analysis using data from WRIST, which had longitudinal data from a robust collection of covariates for patients who underwent surgery and casting. Among the 296 patients recruited in the WRIST study, 59% (174) of patients (mean age 71 ± 9 years) with complete sociodemographic data and 12-month follow-up for each primary outcome were included in the main analysis. More patients underwent surgery than casting (72% [126 of 174] versus 28% [48 of 174]). Most sociodemographic variables were similar between the surgery and casting groups, except for age and volar tilt. The surgical cohort was composed of patients randomized to external fixation, closed reduction percutaneous pinning, or volar locking plate internal fixation. The casting cohort consisted of patients who elected to be treated with closed reduction and casting. A tree-based reinforcement statistical learning method was used to determine the best treatment, either surgery or casting, to maximize functional and esthetic outcomes while minimizing pain. Tree-based reinforcement learning is a statistical learning method to build an unsupervised decision tree within a causal inference framework that will identify useful variables and their cutoff values to tailor treatment assignment accordingly to achieve the best health outcome desired. The primary outcome was minimization of pain (12-month Michigan Hand Outcomes Questionnaire pain subdomain score), maximization of grip strength, total ROM (supination and wrist arc of motion), and esthetics (12-month Michigan Hand Outcomes Questionnaire esthetics subdomain score). RESULTS: Casting was the best treatment to reduce pain and maximize esthetics, whereas surgery maximized grip strength and ROM. When the patient favored gaining ROM over pain reduction (more than 80:20), surgery was the preferred treatment. When the patient prioritized the importance of grip strength over pain reduction (more than 70:30), surgery was also the preferred treatment. CONCLUSION: There are tradeoffs in outcomes after treating patients older than 60 years with closed extraarticular distal radius fractures with casting or surgery. When patients are attempting to balance minimizing pain and improving functional outcomes, unless they desire maximal functional recovery, casting may be the better treatment. Surgery may be beneficial if patients want to regain as much grip strength and ROM as possible, even with the possibility of having residual pain. These findings can be referenced for more concrete preoperative counseling and patient expectation management before treatment selection. LEVEL OF EVIDENCE: Level III, therapeutic study.
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 | 0,069 | 0,111 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».