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Record W2966423040 · doi:10.1177/2325967119s00259

Prevalence of Clinical Depression among Patients after Shoulder Stabilization Repair: A Prospective Study

2019· article· en· W2966423040 on OpenAlexaboutno aff
Danielle Weekes, Richard E. Campbell, Nicholas J. Giunta, Matthew D. Pepe, Bradford S. Tucker, Michael G. Ciccotti, Kevin B. Freedman, William Emper, Fotios P. Tjoumakaris

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDepression (economics)Major depressive disorderPhysical therapyProspective cohort studyCohortInternal medicinePsychiatryMood

Abstract

fetched live from OpenAlex

Objectives: Young, athletic patients who sustain a musculoskeletal injury can suffer from major depressive disorder (MDD), either pre-existing the injury, or in response to injury. Depression can have deleterious effects on mental and physical well-being, and can ultimately lead to suicide. In fact, suicide may represent over 7% of deaths in young, high-level athletes. Therefore, it is paramount among surgeons to recognize vulnerable patient populations. The purpose of the present investigation was to determine the prevalence of MDD in patients with shoulder instability and its’ effect on outcomes in patients undergoing primary arthroscopic shoulder stabilization. Methods: Eighty-eight patients undergoing primary arthroscopic shoulder stabilization were prospectively enrolled and queried at 6 weeks, 3 months, 6 months, and 1 year. Depression symptoms were assessed with the Quick Inventory of Depressive Symptomatology (QIDS-SR16). MDD diagnosis was defined as a QIDS-SR16 score ≥6. Shoulder functionality was assessed with the Western Ontario Shoulder Instability Index (WOSI). Patients were grouped based on their MDD symptomatology preoperatively into MDD and Non-MDD groups. T-test analysis was used to compare outcomes between the groups. Results: The average age of patients on the day of surgery was 29.9 years old. Seventy-four (84.1%) participants were male, while 14 (15.9%) were female. Of the 88 patients enrolled, 44 (50%) met MDD criteria. Baseline averaged WOSI scores for the MDD cohort were worse than the non-MDD group (p= 0.016), 64.9% and 55.0%, respectively. Shoulder function, measured via the WOSI score, significantly improved throughout the study except at the 6-week follow-up point; however, the MDD group continued to have worse shoulder function at 6 weeks post-op (p= 0.04), 6 months post-op (p=0.03) and 1 year post-op (p< 0.01). There was no significant difference in mean WOSI score between the MDD and non-MDD group at 3 months (p= 0.16). WOSI scores at 1-year for the MDD and non-MDD cohort were 21.1% and 8.9%, respectively. MDD diagnosis increased at the 6-week time point (p= 0.023); however, it declined during the rest of the study period (p< 0.01). Conclusion: A significant proportion of patients with shoulder instability exhibit depression symptoms (50% in this series). Our results suggest that pre-operative depression negatively correlates with shoulder outcome functionality. Interestingly, arthroscopic shoulder stabilization can lead to post-operative depression; however, by 3-months there is a strong reversal of this effect, with significant reduction of depression symptoms in all patients. This effect may be secondary to the significant physical limitations caused by shoulder immobilization protocols for the first 6 weeks. As patients regain shoulder strength, stability and function, they exhibit less depression symptoms, indicating surgical intervention can significantly decrease depression symptoms that are secondary to musculoskeletal injuries.

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)0.002
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.338
Teacher spread0.319 · 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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Citations2
Published2019
Admission routes1
Has abstractyes

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