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Record W3004526418 · doi:10.1177/2325967119894738

The Prevalence and Clinical Implications of Comorbid Back Pain in Shoulder Instability: A Multicenter Orthopaedic Outcomes Network (MOON) Shoulder Instability Cohort Study

2020· article· en· W3004526418 on OpenAlexaboutno aff
Kevin Cronin, Brian R. Wolf, Justin A. Magnuson, Cale A. Jacobs, Shannon F. Ortiz, Julie Y. Bishop, Matthew Bollier, Keith M. Baumgarten, Jonathan T. Bravman, Robert H. Brophy, Charles L. Cox, Brian T. Feeley, John A. Grant, Grant L. Jones, John E. Kuhn, C. Benjamin, Robert G. Marx, Eric C. McCarty, Bruce S. Miller, Adam J. Seidl, Matthew V. Smith, Rick W. Wright, Alan L. Zhang, Carolyn M. Hettrich

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCohortPhysical therapyInstabilityCohort studyPhysical medicine and rehabilitationInternal medicine

Abstract

fetched live from OpenAlex

Background: Understanding predictors of pain is critical, as recent literature shows that comorbid back pain is an independent risk factor for worse functional and patient-reported outcomes (PROs) as well as increased opioid dependence after total joint arthroplasty. Purpose/Hypothesis: The purpose of this study was to evaluate whether comorbid back pain would be predictive of pain or self-reported instability symptoms at the time of stabilization surgery. We hypothesized that comorbid back pain will correlate with increased pain at the time of surgery as well as with worse scores on shoulder-related PRO measures. Study Design: Cross-sectional study; Level of evidence, 3. Methods: As part of the Multicenter Orthopaedic Outcomes Network (MOON) Shoulder Instability cohort, patients consented to participate in pre- and intraoperative data collection. Demographic characteristics, injury history, preoperative PRO scores, and radiologic and intraoperative findings were recorded for patients undergoing surgical shoulder stabilization. Patients were also asked, whether they had any back pain. Results: The study cohort consisted of 1001 patients (81% male; mean age, 24.1 years). Patients with comorbid back pain (158 patients; 15.8%) were significantly older (28.1 vs 23.4 years; P < .001) and were more likely to be female (25.3% vs 17.4%; P = .02) but did not differ in terms of either preoperative imaging or intraoperative findings. Patients with self-reported back pain had significantly worse preoperative pain and shoulder-related PRO scores (American Shoulder and Elbow Surgeons score, Western Ontario Shoulder Instability Index) ( P < .001), more frequent depression (22.2% vs 8.3%; P < .001), poorer mental health status (worse scores for the RAND 36-Item Health Survey Mental Component Score, Iowa Quick Screen, and Personality Assessment Screener) ( P < .01), and worse preoperative expectations ( P < .01). Conclusion: Despite having similar physical findings, patients with comorbid back pain had more severe preoperative pain and self-reported symptoms of instability as well as more frequent depression and lower mental health scores. The combination of disproportionate shoulder pain, comorbid back pain and mental health conditions, and inferior preoperative expectations may affect not only the patient’s preoperative state but also postoperative pain control and/or postoperative outcomes.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.354
Teacher spread0.310 · 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".

Quick stats

Citations5
Published2020
Admission routes1
Has abstractyes

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