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

PREDICTORS OF EPISODE-OF-CARE COSTS FOR ANKLE FRACTURES

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

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsOrthopedic surgeryAnkleContext (archaeology)Indirect costsActivity-based costingTrauma centerPerioperativeRetrospective cohort studyInternal fixationTotal cost

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.273
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes2
Has abstractyes

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