141 Terminology and diagnostic criteria used in clinical studies investigating subacromial impingement syndrome: a scoping review
Bibliographic record
Abstract
<h3>Introduction</h3> Lack of consensus regarding terminology and diagnostic criteria used to describe and identify patients with subacromial impingement syndrome (SIS) could be an important driver of misconceptions and misinterpretations of scientific results in this population. We aim to map the literature regarding terminology and diagnostic criteria used in clinical studies investigating SIS. <h3>Materials and Methods</h3> PubMed, Embase, CINAHL and SPORTDiscus were searched from inception to June 2020 using known terms for SIS. Peer-reviewed clinical studies investigating SIS were eligible for inclusion. Studies containing secondary analyses of a previously published study, reviews, pilot studies and studies with less than ten participants were excluded. Two reviewers independently screened titles and abstracts, three reviewers independently applied inclusion and exclusion criteria to full-text versions of the articles and one reviewer extracted data. Disagreement between the reviewers was resolved through dialogue. <h3>Results</h3> 11.056 records were identified. 911 were retrieved for full-text screening. 535 were included. 20 different terms for SIS were identified. The diagnostic criteria were generally based on a cluster of pain provocative shoulder tests. 134 different diagnostic criteria were identified. 30% of the studies used a combination of clinical tests and imaging. 9% of the studies specified that they included patients with full-thickness supraspinatus tears and 46% specified that they did not. <h3>Conclusion</h3> There is a worrying lack of consensus regarding terminology and diagnostic criteria for SIS. This calls for careful consideration when interpreting the results of studies investigating SIS and when comparing studies.
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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 | 0.083 | 0.294 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.060 | 0.054 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".