Predictors of Dislocation and Revision After Shoulder Stabilization in Ontario, Canada, From 2003 to 2008
Bibliographic record
Abstract
BACKGROUND: Factors contributing to recurrent dislocation, revision stabilization, and complications requiring reoperation after an initial shoulder stabilization procedure for instability have not been evaluated on a population level. PURPOSE: (1) To define the rate of ipsilateral revision stabilization, contralateral primary stabilization, postoperative dislocation, and complications after primary shoulder stabilization in a population cohort. (2) To understand which risk factors among patient, surgical, and provider factors influence these outcomes. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: All residents of Ontario, Canada, aged 16 to 60 years undergoing primary shoulder stabilization between July 2003 and December 2008 were identified from billing and hospital databases. Separate Cox proportional hazards survivorship models were built for the outcomes revision stabilization and postoperative physician-documented shoulder relocation (minimum 2-year follow-up). Model covariates included patient demographics (age, sex, preoperative dislocations), provider characteristics (surgeon volume, hospital academic status), and type of surgery (open, arthroscopic). The frequency and risk factors for contralateral stabilization were identified. RESULTS: A total of 5904 patients (80.6% male; median age, 29 years) were identified. Arthroscopic stabilization was used in ~60% of cases in 2003, increasing to ~80% in 2008. The rates of postoperative dislocation were 6.9%, revision stabilization 4%, and contralateral primary stabilization 3.9%. Patients aged younger than 20 years had a 7.7% revision rate (hazard ratio [HR], 2.7; 95% confidence interval [CI], 1.7-4.2; P < .0001) and a 12.6% rate of postoperative physician-documented dislocation (HR, 2.4; 95% CI, 1.8-3.4; P < .0001), compared with 2.8% and 5.5%, respectively, in patients 29 years old (median cohort age). Patients with 3 or more preoperative dislocations in Ontario had an increased risk of revision (HR, 2.1; 95% CI, 1.5-3.0; P < .0001) and postoperative dislocation (HR, 10.6; 95% CI, 8.1-14.0; P < .0001). Revision was more common after arthroscopic (4.3%) compared with open (3.5%) stabilization (HR, 1.4; 95% CI, 1.02-1.98; P = .04). No provider factor was predictive, including surgeon volume. Reoperation rate for complications not related to recurrent instability was 0.23% (infection, 0.07%; manipulation under anesthesia, 0.15%). CONCLUSION: The risks of revision stabilization and postoperative (either shoulder) dislocation were most influenced by young age (<20 years) and having had 3 or more preoperative dislocations. Complications requiring surgery are rare.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 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".