MétaCan
Menu
Back to cohort
Record W2111330956 · doi:10.3233/wor-2009-0937

Why have we not solved the MSD problem?

2009· article· en· W2111330956 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWork · 2009
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Despite worldwide attention for more than four decades, musculoskeletal disorders (MSDs) remain a substantial concern at work and result in considerable personal and societal burden. This slow progress is not for want of trying. Prevention of MSDs has been emphasized in multiple jurisdictions. For example, in 2007 the European Agency for Safety and Health at Work organized a major campaign, "Lighten the Load -How to prevent Musculoskeletal Disorders (MSDs)" and NIOSH in the USA specifically identified MSDs as a major focus in their National Occupational Research Agenda. However, the results from surveys, published sick leave, and lost time data indicate we have a way to go in preventing MSDs. Which leaves us with a question: Why have we not solved the MSD problem?

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.647
Threshold uncertainty score0.161

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.271
Teacher spread0.256 · 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