Risk factors for low back pain in active military personnel: a systematic review
Bibliographic record
Abstract
PURPOSE: Low back pain (LBP) is prevalent in military personnel. We aimed to systematically review the literature regarding risk factors for first-time LBP during military service among active duty military personnel. METHODS: We searched six electronic databases (inception-April 2020) for randomised controlled trials, cohort studies, and case-control studies published in English in peer-reviewed journals. Eligible studies were independently critically appraised by paired reviewers and a descriptive synthesis was conducted. RESULTS: We screened 1981 records, reviewed 118 full-text articles, and synthesised data from eight acceptable quality cohort studies. Studies assessed physical (n = 4), sociodemographic (n = 2), and/or occupational factors (n = 5) associated with LBP. Two studies reported prior LBP was associated with a greater than twofold increased risk of LBP compared to those without prior LBP. Other factors consistently associated with LBP included previous musculoskeletal injury (n = 2), less time spent on physical training (n = 2), female sex (n = 2), and lower rank (n = 2). Factors associated with LBP from single studies included marital status, lower education level, blast injury, job duties, and service type. We found inconsistent associations for performance on physical fitness tests, age, and occupation type. Psychological risk factors were not assessed in any included studies. CONCLUSION: In active duty personnel, prior history of LBP, previous musculoskeletal injury, less time in physical training, female sex, and lower rank were consistent risk factors for LBP. This information is relevant for researchers, active duty military personnel, and other decision makers. Future studies should explore causal relationships for LBP in this population. PROSPERO registration number: CRD42018084549.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".