Prevalence of chronic musculoskeletal pain among active and retired military personnel: a systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to assess the prevalence of musculoskeletal chronic pain among active and retired members of military forces and to characterize potential factors that could influence the frequency of musculoskeletal chronic pain. INTRODUCTION: Inherent to military occupations is a diverse source of occupational hazards that affect the well-being of individuals at any stage of their military career (active and retired). Chronic pain may result from the continuous exposure to physically demanding tasks and other risks. Moreover, chronic pain has been associated with other comorbidities, including mental health conditions. INCLUSION CRITERIA: Scientific papers in French or English reporting on chronic pain derived from a musculoskeletal cause in active and retired military personnel will be considered. There will be no restriction on participants' country, age, or length of service. METHODS: The published literature will be identified by exploring biomedical, pharmacological, and physiology bibliographic databases. The unpublished literature will be located through the search of thesis and gray literature repositories. This review will follow the condition-context-population approach and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The extracted data will include any detail about the anatomic location, diagnosis, types of studies, and risk factors. The review will follow JBI methods of critical appraisal, data extraction, and data synthesis for reviews containing prevalence data. If enough evidence is found, meta-regression analysis will performed, otherwise a narrative review will be completed. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42020153704.
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 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 | 0.057 | 0.055 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.059 | 0.007 |
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".