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Record W4389902177 · doi:10.1136/military-2023-002461

Are military fitness tests safe for members with a total hip arthroplasty?

2023· article· en· W4389902177 on OpenAlexafffundabout
Max Talbot, Lesleigh E. Pullman, M Sokolov, Tara Reilly, Ryan Russell, C. A. Dion, Daniel Théoret, Gerard P. Slobogean

Bibliographic record

VenueBMJ Military Health · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsCanadian Armed ForcesDepartment of National Defence
FundersMinistère de la Défense NationaleCanadian Armed Forces
KeywordsTotal hip arthroplastyArthroplastyHip arthroplastyMedicinePhysical therapySurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: Canadian Armed Forces (CAF) members must complete an annual fitness evaluation. Members with a total hip arthroplasty (THA) may be at risk of injury during these strenuous tests. To inform CAF policy, we sought expert consensus on the safety of fitness testing for members with a THA. METHODS: We conducted a three-round Delphi study with a panel of hip arthroplasty experts to determine the safety of CAF operational fitness evaluations for members with a THA. The experts evaluated videos of the 10 individual tasks included in the evaluations. RESULTS: All individual tasks were judged to be safe by consensus. One task, which involves digging with a shovel, was considered safe provided that participants avoid deep hip flexion. The nine other tasks were judged to be safe without modifications or interventions. The experts also supported a policy recommendation that would allow members to perform military fitness evaluations if they (1) have a primary THA, (2) had no episodes of instability, (3) are at least 12 months postoperatively and (4) have been cleared by an orthopaedic surgeon and a physiatrist/physiotherapist. CONCLUSION: A panel of arthroplasty experts concluded, based on video analysis, that CAF fitness evaluations are generally safe for members with a THA.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.088
GPT teacher head0.453
Teacher spread0.365 · 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.

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
Published2023
Admission routes3
Has abstractyes

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