Does Study Design Affect Redislocation Rates After Primary Shoulder Dislocations? A Systematic Review Comparing Prospective and Retrospective Studies
Bibliographic record
Abstract
PURPOSE: To compare recurrence rates between prospectively collected and retrospectively collected data on primary anterior shoulder dislocations, as this could influence the timing of surgical decision making. METHODS: A comprehensive literature search of Medline, Embase, CINAHL, and hand searches was performed. Recurrence rates of anterior shoulder dislocations were collected from relevant articles, along with follow-up length, age, and gender. An independent sample t test was conducted to evaluate our hypothesis. A multiple linear regression model was used to examine the variance in recurrence rates while controlling for covariates. RESULTS: A total of 1,379 articles were identified, of which 25 were relevant to our study-16 prospective and 9 retrospective. The average rate of recurrence of anterior shoulder dislocations in retrospective studies (mean [M] = 45.2, standard deviation [SD] = 31.67) was not significantly different from that in prospective studies (M = 56.7, SD = 22.55). The 95% confidence interval for the difference of the means ranged from -34.05 to 10.91. After controlling for covariates with the multiple linear regression, only 1.9% of the variance in recurrence rates was due to study type and was not significant (P = .42). The t test performed to evaluate our hypothesis was also not significant t(23) = -1.07, P = .298. CONCLUSIONS: When comparing prospective and retrospective studies, there was no significant difference in recurrence rates of primary anterior shoulder dislocations treated nonoperatively. The average redislocation rate was 56.7% in prospective studies and 45.2% in retrospective studies. Furthermore, the majority of this difference was accounted for by varying rates between age groups. Further research is needed to determine the risk of redislocation in specific age groups, to guide treatment decisions based on varying risk. LEVEL OF EVIDENCE: Systematic review of Level II and III studies.
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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 | 0.079 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".