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Record W4392365081 · doi:10.1016/j.jse.2024.01.037

Arthroscopic stabilization surgery for first-time anterior shoulder dislocations: a systematic review and meta-analysis

2024· review· en· W4392365081 on OpenAlexaff
Hassaan Abdel Khalik, Darius L. Lameire, Timothy Leroux, Mohit Bhandari, Moin Khan

Bibliographic record

VenueJournal of Shoulder and Elbow Surgery · 2024
Typereview
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsImpactUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineSurgeryAnterior shoulderMeta-analysisInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The optimal management of first-time anterior shoulder dislocations (FTASDs) remains controversial. Therefore, the purpose of this study was to assess the efficacy of arthroscopic stabilization surgery for FTASDs through a systematic review and meta-analysis of existing literature. METHODS: MEDLINE, Embase, and Web of Science were searched from inception to December 18, 2022, for single-arm or comparative studies assessing FTASDs managed with arthroscopic stabilization surgery following first-time dislocation. Eligible comparative studies included studies assessing outcomes following immobilization for an FTASD, or arthroscopic stabilization following recurrent dislocations. Eligible levels of evidence were I to IV. Primary outcomes included rates of shoulder redislocations, cumulative shoulder instability, and subsequent shoulder stabilization surgery. RESULTS: Thirty-four studies with 2222 shoulder dislocations were included. Of these, 5 studies (n = 408 shoulders) were randomized trials comparing immobilization to arthroscopic Bankart repair (ABR) after a first dislocation. Another 16 studies were nonrandomized comparative studies assessing arthroscopic Bankart repair following first-time dislocation (ABR-F) to either immobilization (studies = 8, n = 399 shoulders) or arthroscopic Bankart repair following recurrent dislocations (ABR-R) (studies = 8, n = 943 shoulder). Mean follow-up was 59.4 ± 39.2 months across all studies. Cumulative loss to follow-up was 4.7% (range, 0%-32.7%). A composite rate of pooled redislocation, cumulative instability, and reoperations across ABR-F studies was 6.8%, 11.2%, and 6.1%, respectively. Meta-analysis found statistically significant reductions in rates of redislocation (odds ratio [OR] 0.09, 95% confidence interval [CI] 0.04-0.3, P < .001), cumulative instability (OR 0.05, 95% CI 0.03-0.08, P < .001), and subsequent surgery (OR 0.08, 95% CI 0.04-0.15, P < .001) when comparing ABR-F to immobilization. Rates of cumulative instability (OR 0.32, 95% CI 0.22-0.47, P < .001) and subsequent surgery rates (OR 0.27, 95% CI 0.09-0.76, P = .01) were significantly reduced with ABR-F relative to ABR-R, with point estimate of effect favoring ABR-F for shoulder redislocation rates (OR 0.59, 95% CI 0.19-1.83, P = .36). Return to sport rates to preoperative levels or higher were 3.87 times higher following ABR-F compared to immobilization (95% CI 1.57-9.52, P < .001), with limited ABR-R studies reporting this outcome. The median fragility index of the 5 included randomized controlled trials (RCTs) was 2, meaning reversing only 2 outcome events rendered the trials' findings no longer statistically significant. CONCLUSION: Arthroscopic stabilization surgery for FTASDs leads to lower rates of redislocations, cumulative instability, and subsequent stabilization surgery relative to immobilization or arthroscopic stabilization surgery following recurrence. Although a limited number of RCTs have been published on the subject matter to date, the strength of their conclusions is limited by a small sample size and statistically fragile results.

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 imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.033
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.034
Bibliometrics0.0100.010
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.124
GPT teacher head0.395
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations18
Published2024
Admission routes1
Has abstractyes

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