Discordance Between Population Impact of Musculoskeletal Disorders and Scientific Representation: A Bibliometric Study
Bibliographic record
Abstract
OBJECTIVE: Musculoskeletal disorders (MSDs) are a leading cause of healthy years lost due to premature mortality and disability. Our objective was to investigate whether MSDs were commensurably represented within the published health literature. METHODS: MEDLINE bibliometric data were retrieved for 2011 and 2016. The 25 disease branches, including MSDs, were ranked according to published article counts, proportion of all publications, and increase in publications from 2011 to 2016. Rankings were also considered within 5 groupings of general health journals: geriatrics and gerontology, general and internal medicine, multidisciplinary sciences, primary health care, and public health. RESULTS: There were 532,283 MEDLINE publications in 2016, a 16% increase over 2011. In 2016, MSDs ranked 13th in publication count, unchanged from 2011. The increase of 11% in MSD publications from 2011 was below the overall increase. Of 2016 publications, only 7% were MSD indexed, dropping from 7.3% in 2011. MSD-indexed publications had their highest ranking (8th) within geriatrics and gerontology, and lowest (19th) within public health. CONCLUSION: MSDs appear underrepresented in the published health literature generally, and specifically within public health, despite their significant population impact. A broader focus on noncommunicable diseases associated with mortality omits noncommunicable diseases such as MSDs that are leading contributors to high morbidity and high costs, and such omission likely contributes to the neglect of recognizing MSDs as a health priority.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.135 | 0.192 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".