Diagnostic Validity of Patient-Reported History for Shoulder Pathology
Bibliographic record
Abstract
Objective The purpose of this article is to determine whether patient-reported history items are predictive of shoulder pathology and have the potential for use in triaging patients with shoulder pathology to orthopaedic outpatient clinics. Setting It is set at two tertiary orthopaedic clinics. Patients All new patients reporting pain and/or disability of the shoulder joint were prospectively recruited. A total of 193 patients were enrolled, 15 of whom withdrew, leaving 178 patients composing the study sample. Design Patients completed a questionnaire on the history of their pathology, then the surgeon took a thorough history indicating the most likely diagnosis. The clinician then performed appropriate physical examination. Arthroscopy was the reference standard for those undergoing surgery and magnetic resonance imaging (MRI) with arthrogram for all others. We calculated the sensitivity, specificity, and likelihood ratios (LRs) of history items alone and in combination. We used the LRs to generate a clinical decision algorithm. Main Outcome Measures Diagnosis was determined through arthroscopy or MRI arthrogram. Reporting was standardized to ensure review of all structures. Results The physical examination and history agreed in 75% of cases. Of those that did not agree, the physical examination misdirected the diagnosis in 47% of our cases. In particular, history items were strong predictors of anterior and posterior instability and subscapularis tears and were combined in a tool to be utilized for screening patients. Conclusion The patient-reported history items were effective for diagnosing shoulder pathology and should be considered for use in a triaging instrument.
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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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".