EFFECTS OF OBSERVER ON THE DIAGNOSTIC ACCURACY OF LOW‐FIELD <scp>MRI</scp> FOR DETECTING CANINE MENISCAL TEARS
Bibliographic record
Abstract
Low-field MRI (lfMRI) has become increasingly accepted as a method for diagnosing canine meniscal tears in clinical practice. However, observer effects on diagnostic accuracy have not been previously reported. In this study, 50 consecutive stifle joints with clinical and radiologic evidence of cranial cruciate ligament insufficiency were investigated by lfMRI and arthroscopy. Fifteen observers who had varying levels of experience and who were unaware of arthroscopic findings independently reviewed lfMRI studies and recorded whether lateral and medial meniscal tears were present. Diagnostic accuracy (sensitivity, specificity, positive (PPV) and negative predictive value (NPV)) was determined for each observer and median values were calculated for all observers, using arthroscopy as the reference standard. Interrater agreement was determined based on intraclass correlation coefficient (ICC) analysis. Observer level of experience was compared with diagnostic sensitivity and specificity using correlation analysis. Based on pooled data for all observers, median sensitivity, specificity, PPV, and NPV for lfMRI diagnosis of lateral meniscal tears were 0.00, 0.94, 0.05, and 0.94, respectively. Median sensitivity, specificity, PPV, and NPV for medial meniscal tears were 0.74, 0.89, 0.83, and 0.79, respectively. Interrater agreement for all menisci was fair (0.51). Menisci were less consistently scored as having no tears (ICC = 0.13) than those scored as having tears (ICC = 0.50). No significant correlations between observer experience and diagnostic sensitivity/specificity were identified. Findings indicated that the accuracy of lfMRI for diagnosing canine meniscal tears was poor to fair and observer-dependent. Future studies are needed to develop standardized and widely accepted lfMRI criteria for diagnosing meniscal tears.
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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 | 0.049 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".