GNRB® Knee Arthrometer: Inter- and Intra-observer Reliability and Learning Curve
Bibliographic record
Abstract
Background Diagnosing anterior cruciate ligament rupture is challenging, particularly due to the subjective nature of clinical laxity assessments. Objective evaluation methods are necessary for consistency and publication in clinical research. This study aims to assess the reproducibility of the GNRB® knee arthrometer (GeNouRoB, Laval, France) across different examiners and to examine the associated learning curve for a junior examiner. Methods Anterior translation measurements were conducted on 20 healthy knees using the GNRB arthrometer. Two examiners, a senior and a junior, performed the measurements independently and were blinded to each other's results. Measurements were taken at two different push forces (134 N and 200 N). The study evaluated inter- and intra-observer reproducibility using Cohen's kappa coefficient and the intraclass correlation coefficient (ICC). The junior examiner also performed a third series of measurements to assess the learning curve. Results The senior examiner demonstrated excellent reproducibility with an ICC greater than 0.75 for all measurements. The junior examiner's reproducibility varied from fair to good, with an ICC ranging from 0.45 to 0.75. Inter-observer reproducibility between the senior and junior examiners was excellent (ICC >0.75). Notably, the junior examiner's reproducibility improved to an excellent level (ICC >0.75) during the second series of measurements. Conclusion The GNRB system provides a reproducible method for evaluating anterior knee laxity across different examiners. A learning curve of approximately 20 knees is sufficient for a junior examiner to achieve statistically excellent reproducibility.
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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 | 0.066 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".