Analyse de pratiques de l'utilisation des tests tendineux de l'épaule dans la pratique libérale par une enquête quantitative
Bibliographic record
Abstract
INTRODUCTION: Rotator cuff tests validity is poor and recents studies demonstrate their ineffectiveness in shoulder diagnosis. The aim of this survey is to show how these tests are used in practice and to see if their use is in line with the current evidence. METHOD: A cross sectional online survey was conducted and dissaminated through a French private physiotherapists mailing list and social media plateforms from december 2023 to february 2024. RESULTS : 120 french private physiotherapists completed the survey. 72 % used these tests. A quarter of physiotherapists have stopped using the tests because of a lack of precision. Among physiotherapists using the tests, only 25 % uses them exclusively to diagnose a shoulder injury. 65 % of french private physiotherapists uses these tests for diagnosis and also to follow the patient’s progress without targeting a specific structure. DISCUSSION AND CONCLUSION : Rotator cuff tests are still used but their use changed since their first description in line with the literature. French private physiotherapists use the tests for diagnosis but due to the lack of reliability they have evolved their practice. Confonted with the lack of confidence of a positive test, they keep the specifics movements as indicator of patient progress without specifically targeting shoulder structure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.086 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".