Comparing the Interobserver Reliability of 4 Methods Used to Measure Knee Laxity on Coronal Plane Stress Radiograph
Bibliographic record
Abstract
Background: Varus and valgus knee stress radiographs provide valuable information in the pre- and postoperative evaluation of joint laxity in patients with multiligament knee injuries (MLKIs). Purpose: To review the literature for described techniques of quantifying laxity on coronal stress radiographs of the knee and identify the most reliable method. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: A thorough literature search using the MEDLINE and Embase databases identified 4 studies with distinct methods for objectively measuring laxity on varus and valgus stress radiographs: Heesterbeek et al (2008), Jacobsen (1976), LaPrade et al (2004), and Sawant et al (2004). To compare these methods, 200 coronal plane stress radiographs from 50 patients with MLKIs were retrospectively reviewed from an MLKI database at a single institution. The amount of varus and valgus laxity on each radiograph was measured independently by 4 reviewers using each method. Intraclass correlation coefficients (ICCs) with 95% CIs were calculated to assess the interobserver reliability of each method overall and the varus and valgus measurements individually. Results: For all 4 methods, the overall interobserver reliability was considered at least moderate. The method by Heesterbeek et al proved to have the highest interrater reliability in all domains-overall (ICC, 0.87 [95% CI, 0.85-0.90]), valgus (ICC, 0.83 [95% CI, 0.78-0.88]), and varus (ICC, 0.87 [95% CI, 0.83-0.90])-demonstrating good to excellent reliability both overall and in varus measurements and showing good reliability in valgus measurements. The method by Sawant et al demonstrated good reliability in valgus measurements. All other measures demonstrated moderate reliability. Conclusion: Available methods for measuring knee joint laxity on varus and valgus knee stress radiographs in patients with MLKIs demonstrated moderate to good interobserver reliability. The method described by Heesterbeek et al proved to have the highest reliability overall as well as in measurements on varus and valgus views individually.
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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 | 0.053 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".