Correlations between Three Patient-Assessed Shoulder Instability Scales
Bibliographic record
Abstract
PURPOSE: To evaluate the correlations between three patient-assessed shoulder instability scales before and after Latarjet stabilisation for traumatic anteroinferior glenohumeral instability. METHODS: Records of 30 men and 2 women (mean age, 26.7 years) who had not undergone surgery for antero-inferior shoulder instability and records of 31 men and one woman (mean age, 27 years) who had undergone Latarjet stabilisation for anteroinferior shoulder instability and had been followed up for a mean period of 21.3 months were reviewed. Correlations between the Western Ontario Shoulder Instability Index (WOSI), the Melbourne Instability Shoulder Score (MISS), and the L'Insalata Shoulder Questionnaire (L'Insalata) were assessed. RESULTS: The mean score of each scale was significantly greater in the postoperative than preoperative group (p<0.001). Within each group, the mean scores of the 3 scales differed significantly (ANOVA, p=0.001). The mean L'Insalata score was significantly higher than the mean WOSI and MISS scores (p<0.01, posthoc analysis), but the latter 2 scores did not differ significantly (p>0.01, post-hoc analysis). Correlations of all scale pairs were significant (p<0.001). The WOSIMISS correlations in the pre- and post-operative groups were moderate. The L'Insalata-WOSI correlations in the pre- and post-operative groups were moderate and high, respectively. The L'Insalata-MISS correlations in the pre- and post-operative groups were high and moderate, respectively. CONCLUSION: The MISS and WOSI scales are moderately correlated. Correlation of the L'Insalata scale with other scales depends on the operative status of the patient. The use of the L'Insalata scale alone is not recommended.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".