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Record W3049398840 · doi:10.1177/1758573220947025

Prior mood disorder diagnoses do not relate to current mood disorder symptoms or patient-reported disease severity in rotator cuff patients

2020· article· en· W3049398840 on OpenAlexaffabout
Eric Gibson, Justin LeBlanc, Marlis T. Sabo

Bibliographic record

VenueShoulder & Elbow · 2020
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineMoodRotator cuffAnxietyDepression (economics)Medical diagnosisDiseaseMood disordersPsychiatryPhysical therapyHospital Anxiety and Depression ScaleClinical psychologyInternal medicineSurgeryPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Surgery for rotator cuff syndrome does not always produce symptom improvement. Biological factors may explain some symptoms, but mood disorder symptoms may also contribute. The purpose of this study is to examine the interaction between disease severity, prevalence of mood disorder diagnoses, and current mood disorder symptoms in preoperative rotator cuff patients. METHODS: A prospective cohort of patients aged 35-75 years with unilateral rotator cuff disease awaiting surgery participated. Demographics, psychiatric history, the Hospital Anxiety & Depression Scale, and the Western Ontario Rotator Cuff index were collected. Descriptive and univariate statistical testing was performed. RESULTS: Of 140 participants (75M:65W) aged 55 ± 8 years, 34 reported a prior diagnosis of a mood disorder. There was a moderate positive relationship between disease severity and current depression and anxiety scores. Women were more likely to carry a diagnosis of a mood disorder, but there were no differences in current symptom levels between genders. No differences were found in patient-reported outcome measure scores between patients with and without a mood disorder diagnosis. DISCUSSION: Current mood disorder symptoms were associated with greater disease severity, whereas the presence of a past mood disorder diagnosis was not. Awareness of this relationship may reduce bias about past mood disorder diagnoses during decision-making.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.309
Teacher spread0.287 · 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 teacher head, not a consensus.

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 routes2
Has abstractyes

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