Glenoid Track Instability Management Score: Radiographic Modification of the Instability Severity Index Score
Bibliographic record
Abstract
Purpose The purpose of this study is (1) to test the proposed treatment algorithm, the Glenoid Track Instability Management Score (GTIMS), which incorporates the glenoid track concept into the instability severity index score (ISIS), and (2) to compare treatment decision‐making using either GTIMS versus ISIS in 2 cohorts of patients with operatively treated anterior instability. Methods A multicenter, retrospective review of two consecutive groups consisting of 72 and 189 patients treated according to ISIS and GTIMS, respectively, was conducted. Inclusion criteria for all patients were ≥2 confirmed traumatic anterior shoulder instability events and a physical examination demonstrating a positive anterior apprehension and relocation test. The GTIMS was graded for all 189 patients in the cohort, which uses 3‐dimensional computed tomography as the sole radiographic parameter to assess on‐track (0 points) versus off‐track (4 points) Hill‐Sachs lesions. This method differs from ISIS, which uses multiple plain radiographs for the 4‐point imaging portion of the score. Outcomes scores were compared within the GTIMS and ISIS groups, as well as between them for overall comparisons based on the Western Ontario Shoulder Instability Index (WOSI), the Single Assessment Numerical Evaluation (SANE) score, and the mean rates of recurrent instability. Results A total of 261 consecutive patients from 2009 to 2014 who presented with recurrent anterior shoulder instability were treated according to either ISIS (n = 72/261, 27.6%) or GTIMS (n = 189/261, 72.4%). At a mean follow‐up time of 33.2 months (range 24‐49 months), the overall cohort mean ISIS of 2.9 ± 2.2 (range 0‐9) was significantly higher than the mean GTIMS of 1.9 ± 1.9 (range = 0‐9, P < .001). Of the 72 ISIS treated patients, 50 (69.4%) had an ISIS score of ≥ 4 and underwent a Latarjet, and the 22 patients (30.6%) with an ISIS score of < 4 underwent an arthroscopic Bankart repair. Based on GTIMS in the 189‐patient cohort, using the same cutoff of 4 to indicate the need for a Latarjet, 162 patients were treated with arthroscopic Bankart repair (85.7%) and 27 with Latarjet (14.3%). The overall outcomes improved for patients treated with a Latarjet in both groups (GTIMS WOSI from 1099 [47.7% normal] to 395 [81.3% normal]; GTIMS SANE from 48 to 81; ISIS WOSI from 1050 [50% normal] to 345 [83.4% normal]; ISIS SANE from 50 to 84; P < .01). Similar positive outcomes were seen in patients treated with arthroscopic Bankart repair (GTIMS WOSI from 1062 [49.2% normal] to 402 [80.6% normal]; GTIMS SANE from 49 to 82; ISIS WOSI from 1080 [51.8% normal] to 490 [76.7% normal]; ISIS SANE from 48 to 77; P < .01). Of note, the patients with arthroscopically indicated ISIS had significantly worse outcomes scores than those treated arthroscopically according to GTIMS ( P < .01). Of the 189 patients graded with GTIMS, there would have been 33 more Latarjet procedures recommended based on ISIS score. Thus the distribution of procedures based on ISIS versus GTIMS was significantly different (χ 2 = 45.950; P < .001), indicating a higher rate of recommending Latarjets when using ISIS versus GTIMS. Conclusions When ISIS scoring and plain radiograph parameters only are used, this predicted a 2‐fold increase in recommending a Latarjet versus GTIMS scoring criteria, which uses advanced imaging and the on‐ and off‐track principle to more conservatively delineate anterior instability treatment with promising postoperative patient outcomes. Overall, there were minimal differences in outcomes between GTIMS and ISIS Latarjet patients; however, better outcomes were seen in patients indicated for arthroscopic Bankart repair according to GTIMS and on‐off track computed tomography scanning indications. Level of Evidence II, Prospective Cohort Study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".