Accuracy and inter‐observer reliability of visual estimation compared to clinical goniometry of the elbow
Bibliographic record
Abstract
PURPOSE: To test the hypothesis that visual estimation by a trained observer is as accurate and reliable as clinical goniometry for measuring elbow range of motion. METHODS: Instrument validity and inter-observer reliability of visual estimation was evaluated on a consecutive series of 50 elbow contractures. Four observers with different levels of elbow experience first estimated extension and flexion of the contracted elbows and then measured them with a blinded goniometer. RESULTS: Instrument validity for visually-based goniometry was extremely high. ICC scores were 0.97 for both extension and flexion estimations. Systematic error was negligible (1°) with upper limits of agreement being 9° (95% CI: 7°-11°) and 8° (95% CI: 6°-10°), respectively, for extension and flexion. For the expert surgeon, 92% of the visual estimates were within 5° of the value obtained by clinical goniometry. Between experienced observers (elbow surgeon and physician assistant), the ICC's were very high-0.96 for extension and 0.93 for flexion. The systematic errors were low, from -1° to 1° with upper limit of agreement being 11° (95% CI: 8°-14°). However, agreement was poor between an inexperienced study coordinator and the others (ICC's: 0.51-0.38, systematic errors: 8°-18°, upper limit of agreement: 32°-40°). The accuracy of the visual estimations made by the experienced elbow surgeon was as good as the measurements taken with a goniometer by the physician assistant or the clinical fellow and better than those taken by an inexperienced study coordinator. CONCLUSIONS: The trained human eye is highly capable of accurately estimating the range of motion of the elbow, compared to conventional clinical goniometry, depending on the experience of the observer. LEVEL OF EVIDENCE: Diagnostic study, Level II.
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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 | 0.032 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".