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Enregistrement W4415570990 · doi:10.1302/1358-992x.2025.11.020

PREDICTORS OF EPISODE-OF-CARE COSTS FOR ANKLE FRACTURES

2025· article· en· W4415570990 sur OpenAlexaffabout
Simon Martel, J. Montreuil, Gowtham Thangathurai, Greg Berry, Rudy Reindl, Mitchell Bernstein

Notice bibliographique

RevueOrthopaedic Proceedings · 2025
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Policy and Management
Établissements canadiensMcGill University Health Centre
Organismes subventionnairesnon disponible
Mots-clésOrthopedic surgeryAnkleContext (archaeology)Indirect costsActivity-based costingTrauma centerPerioperativeRetrospective cohort studyInternal fixationTotal cost

Résumé

récupéré en direct d'OpenAlex

The perioperative management of trauma patients in the context of a recent focus on value-based healthcare highlights the importance for hospitals to evaluate their episode-of-care costs (EOCC). Despite its large impact on hospital budgets, accurate episode-of-care costs calculation for trauma hospitalizations remains a challenge in Canada. Ankle fractures are one of the most resource-consuming traumatic injuries requiring orthopedic surgery care. Few studies have successfully evaluated the episode-of-care costs of common traumatic orthopedic injuries. The objective of this manuscript is to determine the episode-of-care costs for patients with surgically managed ankle fractures using an activity based costing (ABC) methodology and to assess the patient, injury and surgical factors that affect the episode of care cost of these fractures. A retrospective cohort study of 105 consecutive patients who underwent open reduction internal fixation of an isolated ankle fracture at a Level-1 trauma center in Quebec was conducted. Episode-of-care costs were generated using an activity based costing framework and itemized direct and indirect costs were obtained for every case. The median episode-of-care cost was compared based on patient demographics, injury characteristics and surgical data to identify predictors of episode-of-care costs for surgically treated ankle fractures. The median global episode-of-care cost for ankle fracture surgeries performed at the studied institution was $3,487. On average, 76% of the total costs were attributable to direct costs and 24% of the total costs were attributable to indirect expenditures. Patients aged between 60 and 90 years had a significantly higher median episode-of-care costs ($5051) compared to patients aged between 18 and 29 years ($3,411) and patients aged 30 to 59 years ($3,480) (p = 0.01). The type of ankle injury according to the Lauge-Hansen classification significantly impacted the episode-of-care costs. Supination-adduction (SAD) injuries had significantly higher median episode-of-care costs ($5332) than other types of injuries (p = 0.01). Patient gender, anesthesia type, ASA score and surgeon's fellowship training did not significantly increase the episode-of-care cost in our study. The median episode-of-care cost for patients who underwent surgery within 10 days of their injury was significantly lower than the cost for patients who had their surgery delayed more than 10 days after the injury ($3347 vs. $3634, p=0.03). Subgroup analysis demonstrated that the median direct and indirect costs were increased by $227 and $54 respectively in the group of patients that were delayed more than 10 days before their surgery. There was no difference in age, gender, ASA score or injury type between the two groups. This study provides valuable data on predictors of episode-of-care costs in the surgical management of ankle fractures. Delaying simple ankle fracture cases due to operating room time constraints can increase the total cost and burden of these fractures on the health care system. In addition, this study provides a framework for future episode-of-care cost analysis studies in orthopaedic surgery. Adequate episode-of-care cost analysis is crucial to ensure hospitals and departments receive appropriate funding.

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 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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,867
Score d'incertitude au seuil0,533

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
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)0,0000,000

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 GPT0,273
Écart entre enseignants0,255 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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