<i>Editorial Commentary:</i> Should We Condemn the Shoulder Instability Severity Index Scoring System? Not at All!… Can We Improve Its Radiographic Component? Yes, We Can!
Bibliographic record
Abstract
Is patient selection necessary in shoulder instability surgery? Absolutely. The risk-benefit discussion that the surgeon must have with the patient before proposing an arthroscopic Bankart repair remains crucial to provide informed consent. The most important preoperative risk factors are incorporated in the instability severity index (ISI) score to assist surgeons in the decision-making process. This 10-point score is based on factors derived from a preoperative questionnaire, physical examination, and simple plain radiographs. Using this score at the first visit, the surgeon can explain to the patient and family why a Bankart repair may be contraindicated and why other surgical options may be more suitable. A recent study found that the ISI score has no limited predictive value when applied in a preselected population of military patients without severe bone loss or hyperlaxity. This is not surprising because the authors analyzed a preselected patient population with lower risk than the general population. The value of the ISI scoring system relies on the fact that this tool has been developed after evaluation of arthroscopic Bankart repair in an unselected patient population and that there is no need for sophisticated imaging studies to make the decision. This scoring system should not be condemned but complemented with preoperative advanced imaging studies (computed tomography [CT] scanning or magnetic resonance imaging) to assess the severity of the bone lesions more accurately. Today, the choice of the surgical procedure depends not only on the clinical risk factors included in the ISI score (age, type of sports, level of practice, hyperlaxity) but also on the presence, location and size of bony lesions, as identified and measured on advanced CT scanning images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.026 | 0.029 |
| Insufficient payload (model declined to judge) | 0.011 | 0.013 |
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".