The prevalence of persistent post-traumatic headache in adult civilian traumatic brain injury: a systematic review and meta-analysis on the past 14 years
Bibliographic record
Abstract
ABSTRACT: The most recent prevalence estimate of post-traumatic headache (PTH) after traumatic brain injury (TBI) in veterans and civilians dates back to 2008. The prevalence was found to be 57.8%, with surprising higher rates (75.3%) in mild TBI when compared with those with moderate/severe TBI (32.1%). However, the revision of mild TBI diagnostic criteria and an historic peak of TBI in the elderly individuals attributed to the ageing population may lead to different results. Thus, we conducted a systematic review and meta-analysis to assess the updated prevalence of PTH during the past 14 years only in civilians. A literature search was conducted following PRISMA guidelines guided by a librarian. Screening, full-text assessment, data extraction, and risk of bias assessment were performed blindly by 2 raters. Meta-analysis of proportions using the Freeman and Tukey double arcsine method of transformation was conducted. Heterogeneity, sensitivity analysis, and meta-regressions were performed with the predictors: year of publication, mean age, sex, TBI severity, and study design. Sixteen studies were selected for the qualitative analysis and 10 for the meta-analysis. The overall prevalence estimate of PTH was 47.1%, (confidence interval = 34.6, 59.8, prediction intervals = 10.8, 85.4), being similar at different time points (3, 6, 12, and 36+ months). Heterogeneity was high, and none of the meta-regressions were significant. The overall prevalence of PTH after TBI over the past 14 years remains high even if assessed only in civilians. However, the prevalence rates attributed to mild and moderate/severe TBI were similar, differing significantly from previous reports. Efforts are needed to improve TBI outcomes.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".