Variations in diagnostic criteria for carpal tunnel syndrome among Ontario specialists
Bibliographic record
Abstract
BACKGROUND: Variations in diagnostic criteria for carpal tunnel syndrome (CTS) may result in differing reports of disease prevalence, errors in diagnosis, and variable results of treatment. The objective of this study was to determine how consistent specialists are in their ratings of the importance of clinical criteria for the diagnosis of CTS. METHODS: Three hundred specialist physicians and surgeons received a questionnaire containing 57 clinical criteria for the diagnosis of CTS. A visual analog scale (VAS) was used to rate the importance of each criterion in the diagnosis of CTS. RESULTS: The overall consistency both across and within specialties was poor (intraclass correlation coefficient across specialties (ICC) = 0.28; ICC range within specialties 0.27-0.37). CONCLUSIONS: Specialists are relatively inconsistent in the importance they assign to clinical criteria for the diagnosis of CTS. This inconsistency may be an important source of variation in the reported prevalence and treatment of CTS.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".