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Practice patterns in the care of acute Achilles tendon ruptures

2017· article· en· W2560351938 on OpenAlexaffabout
Ujash Sheth, David Wasserstein, Richard Jenkinson, Rahim Moineddin, Hans J. Kreder, Susan Jaglal

Bibliographic record

VenueThe Bone & Joint Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsAchilles tendonMedicineAchilles tendon rupturePhysical medicine and rehabilitationTendonPhysical therapySurgery

Abstract

fetched live from OpenAlex

AIMS: To determine whether the findings from a landmark Canadian trial assessing the optimal management of acute rupture of the Achilles tendon influenced the practice patterns of orthopaedic surgeons in Ontario, Canada. MATERIALS AND METHODS: Health administrative databases were used to identify Ontario residents ≥ 18 years of age with an Achilles tendon rupture from April 2002 to March 2014. The rate of surgical repair (per 100 cases) was calculated for each calendar quarter. A time-series analysis was used to determine whether changes in the rate were chronologically related to the dissemination of results from a landmark trial published in February 2009. Non-linear spline regression was then used independently to identify critical time-points of change in the surgical repair rate to confirm the findings. RESULTS: A total of 29 531 patients sustained an Achilles tendon rupture during the study period. Consistently, around 21 out of every 100 cases underwent surgical repair up to the first quarter of 2010. However, by the first quarter of 2014, only 6.5 cases per 100 had surgery. A statistically significant decrease in the rate of surgical repair was observed within one year of the presentation of landmark trial results in 2009 (p < 0.001). July 2009 was independently identified as a critical time at which the surgical repair rate began to significantly decline (p < 0.001). The dissemination of trial results was associated with a significant drop in the rate of surgical repair at non-teaching hospitals (p = 0.001). CONCLUSION: 2017;99-B:1629-36.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.023
GPT teacher head0.328
Teacher spread0.305 · 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 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

Citations15
Published2017
Admission routes2
Has abstractyes

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