Glenoid Track Instability Management Score: Radiographic Modification of the Instability Severity Index Score
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».