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Record W7083456857 · doi:10.1177/2325967125s00139

Poster 27: Arthroscopic Labral Repair Results in Lower Recurrence Rates and Is Highly Cost-Effective When Compared to Nonoperative Management for Primary Anterior Shoulder Dislocations: A Contemporary Systematic Review and Decision-Analytic Markov Model-Based Analys

2025· article· en· W7083456857 on OpenAlexaboutno aff

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsAnterior shoulderBankart repairQuality-adjusted life yearMarkov modelMarkov chainProbabilistic logicMarkov chain Monte CarloOutcome (game theory)

Abstract

fetched live from OpenAlex

Objectives: Value based decision making regarding nonoperative management vs. early surgical stabilization for first-time anterior shoulder instability (ASI) events remains controversial. Thus, the purpose of this study was to perform 1) a systematic review of the current literature and 2) a Markov model-based cost-effectiveness analysis comparing an initial trial of nonoperative management to arthroscopic Bankart repair (ABR) for first-time ASI. Methods: A Markov Chain Monte Carlo probabilistic model (Figure 1) was developed to evaluate the outcomes and costs of 1,000 simulated patients (average age » 20 years old) with first-time ASI undergoing nonoperative management vs. ABR. Utility values, recurrence rates, and transition probabilities were derived from the published literature. Costs were determined based on the typical patient undergoing each treatment strategy at our institution. Outcome measures included costs, quality-adjust life-years (QALYs), and the incremental cost-effectiveness ratio (ICER). Probabilities of recurrence as well as the utility of each treatment strategy were obtained from the literature search as described above. Probabilities were pooled across studies to obtain a weighted average for both nonoperative treatment and ABR. Similarly, studies reporting scores on the Western Ontario Shoulder Instability Index (WOSI) scale were utilized to approximate shoulder-related quality of life after each treatment strategy and outcome. Costs for each treatment strategy and transition state in the model were considered from the payer perspective (commercial or government) and obtained from institutional data at a large, academic medical center. Results: The Markov model with Monte Carlo microsimulation demonstrated average (± standard deviation) costs for nonoperative management and ABR of $38,649 ± 10,521 and $43,052 ± 9,352, respectively. Total QALYS acquired over the 10-year time horizon were 7.7 ± 0.4 and 8.4 ± 0.5 for nonoperative management and ABR, respectively. The ICER comparing ABR to nonoperative management was found to be just $5,725/QALY, which falls substantially below the $50,000 willingness-to-pay (WTP) threshold (Figure 2). The mean number of recurrences were 2.6 ± 0.3 and 1.2 ± 0.2 for patients initially assigned to the nonoperative and ABR treatment groups, respectively. Out of 1,000 samples run over 1,000 trials, ABR was the optimal strategy in 98.7% of cases, with nonoperative management the optimal strategy in 1.3% of cases (Figure 3). Conclusions: The primary findings of this systematic review and cost-effectiveness analysis were as follows: 1) when compared to nonoperative management, arthroscopic Bankart repair (ABR) substantially reduces the risk of recurrent dislocations, 2) ABR results in greater shoulder function and higher WOSI scores than does nonoperative management, and 3) despite substantially higher upfront costs, ABR proved to be more cost-effective than an initial trial of nonoperative management, which can be attributed to both fewer recurrent dislocations and greater utility over the long-term.

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)0.009
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.010
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.001

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 head0.016
GPT teacher head0.297
Teacher spread0.281 · 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 designSystematic review
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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