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Record W2504989421 · doi:10.1177/2325967116s00171

Practice Patterns in the Care of Acute Achilles Tendon Ruptures

2016· article· en· W2504989421 on OpenAlexaffabout
Ujash Sheth, David Wasserstein, Rahim Moineddin, Richard Jenkinson, Hans J. Kreder, Susan Jaglal

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineAchilles tendonLogistic regressionAchilles tendon ruptureDemographicsAcute careOdds ratioSurgeryHealth carePhysical therapyEmergency medicineTendonDemographyInternal medicine

Abstract

fetched live from OpenAlex

Objectives: Over the last decade, there has been a growing body of level I evidence supporting non-operative management (focused on early range of motion and weight bearing) of acute Achilles tendon ruptures. Despite this emerging evidence, there have been very few studies evaluating its uptake. Our primary objective was to determine whether the findings from a landmark trial assessing the optimal management strategy for acute Achilles tendon ruptures influenced the practice patterns of orthopaedic surgeons in Ontario, Canada over a 12-year time period. As a second objective we examined whether patient and provider predictors of surgical repair utilization differed before and after dissemination of the landmark trial results. Methods: Using provincial health administrative databases, we identified Ontario residents ≥ 18 years of age with an acute Achilles tendon rupture from April 2002 to March 2014. The proportion of surgically repaired ruptures was calculated for each calendar quarter and year. A time series analysis using an interventional autoregressive integrated moving average (ARIMA) model was used to determine whether changes in the proportion of surgically repaired ruptures were chronologically related to the dissemination of results from a landmark trial by Willits et al. (first quarter, 2009). Spline regression was then used to independently identify critical time-points of change in the surgical repair rate to confirm our findings. A multivariate logistic regression model was used to assess for differences in patient (baseline demographics) and provider (hospital type) predictors of surgical repair utilization before and after the landmark trial. Results: In 2002, ˜19% of acute Achilles tendon ruptures in Ontario were surgically repaired, however, by 2014 only 6.5% were treated operatively. A statistically significant decrease in the rate of surgical repair (p < 0.001) was observed after the results from a landmark trial were presented at a major North American conference (February 2009). Prior to the dissemination of trial results, the odds of undergoing surgical repair at a teaching hospital were found to be significantly higher than if treated at a non-teaching hospital (odds ratio (OR), 1.52; 95% confidence interval (CI), 1.04-2.22; p = 0.03). However, after the landmark trial there was no significant difference in the odds of undergoing surgical repair between teaching and non-teaching hospitals (p = 0.46). All other predictors of surgical repair utilization remained unchanged in the before-and-after analysis. Conclusion: The current study demonstrates that large, well-designed randomized trials, such as the one conducted by Willits et al. can significantly change the practice patterns of orthopaedic surgeons. Moreover, the significant decline in surgical repair rate at both teaching and non-teaching hospitals after the landmark trial suggests both academic and non-academic surgeons readily incorporate high quality evidence in to their practice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.764

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.287
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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