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Record W1999032694 · doi:10.1016/j.arthro.2011.03.012

Patient Specific Factors Affecting the Decision for Surgery for a First Time Anterior Shoulder Dislocation (SS‐09)

2011· article· en· W1999032694 on OpenAlexaboutno aff
Richard C. Mather, Dean C. Taylor

Bibliographic record

VenueArthroscopy The Journal of Arthroscopic and Related Surgery · 2011
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMultivariate analysisQuality of life (healthcare)InstabilityAffect (linguistics)SurgeryPhysical therapyPsychologyNursingInternal medicine

Abstract

fetched live from OpenAlex

Introduction Recurrent instability following a first‐time anterior shoulder dislocation (FTASD) is very common and approaches 100% in some reports. Arthroscopic stabilization can decrease the rate of recurrent instability and improve outcomes, but the decision for surgery remains complex. Multiple patient and provider specific factors exist that affect this treatment algorithm. In this paper, we examine the interaction of these complex factors and provide threshold values at which either surgery or non‐operative treatment is preferred. Methods A Markov Monte Carlo decision model comparing non‐operative versus surgical treatment for a FTASD was constructed using TreeAge Pro (Williamstown, MA, 2007). Four health states were incorporated into the model: initial dislocation, stable shoulder, recurrent instability and revision stabilization. Input parameters included patient‐specific variables (age, gender, activity level, time lost from work or sport, and coping with instability) and physician‐specific variables such as success of surgery as well as outcome probabilities and effectiveness. Values were derived from the literature or estimated by expert opinion where necessary. The primary outcome, treatment‐related quality of life years, was calculated based upon the Western Ontario Shoulder Instability index (WOSI). Specific factors examined were age, gender, time lost from work or sport, surgical success rate, activity level and ability to cope with instability. Multivariate sensitivity analyses were performed to identify key variables that influenced the preferred treatment strategy. Results Surgery for a FTASD was preferred for all men age 15‐35 and for women age 30 and younger. For high‐risk patients such as overhead athletes, surgery was preferred for all men age 15‐35 and women age 33 and younger. Non‐operative management was preferred when the relative risk of dislocation after surgery rises above 0.7 and 0.5, for men and women, respectively. Non‐operative management was preferred for men with an unstable shoulder when they were comfortable with a 14 point drop in WOSI score or when the WOSI benefit of surgery lasted 2.1 years or less. For women these values were 8 WOSI points and 3.6 years. Time lost from work or sport strongly effected the decision for surgery. This relationship is displayed in Figure 1, a two‐way sensitivity analysis of time lost from work or sport against age at first dislocation. Conclusion Surgery for a first time anterior shoulder dislocation resulted in improved outcomes (WOSI) for all men and women under age 30 and was therefore the preferred treatment. Beyond age 30, patient and provider specific factors exerted strong influences on the decision for surgery. Our study clarified the relationship among these factors and indicated threshold values at which surgery resulted in improved outcomes. These thresholds could potentially be useful for guiding clinical decision‐making, designing future studies, or benchmarking.

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)0.004
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.034
GPT teacher head0.281
Teacher spread0.247 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2011
Admission routes1
Has abstractyes

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