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Record W1590059095 · doi:10.3233/wor-2009-0940

The epidemiology of upper extremity musculoskeletal symptoms on a college campus

2009· article· en· W1590059095 on OpenAlexaff
Cammie Chaumont Menéndez, Benjamin C. Amick, Che‐hsu Chang, Jack T. Dennerlein, Ronald B. Harrist, Mark Jenkins, Michelle M. Robertson, Jeffrey N. Katz

Bibliographic record

VenueWork · 2009
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsInstitute for Work & Health
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesU.S. Public Health ServiceCenters for Disease Control and PreventionNational Institutes of Health
KeywordsMedicinePhysical therapyOdds ratioMusculoskeletal painEpidemiologyOddsInternal medicineLogistic regression

Abstract

fetched live from OpenAlex

OBJECTIVE: The study examines temporal variations in upper-extremity musculoskeletal symptoms throughout the day, over a week and throughout the semester. METHODS: 30 undergraduates were followed in a repeated measures study throughout a semester. Upper extremity musculoskeletal symptoms data were collected on handheld computers randomly throughout the day for seven days over three data collection periods. Multilevel statistical models evaluated associations between time-related predictors and symptoms. RESULTS: In adjusted models, pain reported at baseline was associated with increased odds of experiencing both any symptoms (OR=15.64; 90% CI 7.22-33.88) and moderate or greater symptoms (OR=16.44; 90% CI 4.57-29.99). Any symptoms were less likely to be reported if responses occurred at 58-76 days (OR=0.66; 90% CI 0.49-0.86), 77-90 days (OR=0.29; 90% CI 0.20-0.42) and 91-117 days (OR=0.54; 90% CI 0.39-0.75) into the semester compared to 35-57 days. Similarly, responding after midnight was associated with greater odds of reporting moderate or greater symptoms (OR=21.33; 90% CI 6.49-65.97). There was no association observed for day of week and symptoms. CONCLUSION: This pilot work suggests upper extremity musculoskeletal symptoms exhibit temporal variations related to time of day and days into semester. Understanding the natural history of musculoskeletal symptoms and disorders is needed when designing epidemiologic research and/or intervention studies using symptom outcome measures.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.305
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.302
Teacher spread0.291 · 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.

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

Citations7
Published2009
Admission routes1
Has abstractyes

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