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Record W4400801460 · doi:10.1093/milmed/usae353

Evaluating the Phenotypic Patterns of Post-Traumatic Headache: A Systematic Review of Military Personnel

2024· review· en· W4400801460 on OpenAlexaboutno aff
Hannah Lyons, Matilde Sassani, Mark Thaller, Andreas Yiangou, Olivia Grech, Susan P. Mollan, Duncan Wilson, Samuel J. E. Lucas, James Mitchell, Lisa J. Hill, Alexandra J. Sinclair

Bibliographic record

VenueMilitary Medicine · 2024
Typereview
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsnot available
FundersMinistry of DefenceGuarantors of BrainSir Jules Thorn Charitable TrustAssociation of British NeurologistsNational Institute for Health and Care Research
KeywordsMedicineHeadachesInternational Classification of Headache DisordersPopulationMigraineTraumatic brain injuryMilitary personnelCohortInclusion and exclusion criteriaSystematic reviewPhysical therapyMEDLINEPediatricsPsychiatryInternal medicinePathologyAlternative medicineEnvironmental health

Abstract

fetched live from OpenAlex

INTRODUCTION: Mild traumatic brain injury (TBI) affects a significant number of military personnel, primarily because of physical impact, vehicle incidents, and blast exposure. Post-traumatic headache (PTH) is the most common symptom reported following mild TBI and can persist for several years. However, the current International Classification of Headache Disorders lacks phenotypic characterization for this specific headache disorder. It is important to appropriately classify the headache sub-phenotypes as it may enable more targeted management approaches. This systematic review seeks to identify the most common sub-phenotype of headaches in military personnel with PTH attributed to mild TBI. METHODS: We conducted a systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines, focusing on the military population. PubMed, Web of Science, Cochrane, and Clinicaltrials.gov databases were searched. Abstracts and full texts were independently reviewed by two authors using predefined inclusion and exclusion criteria. Data extraction was performed using a standardized form. The risk of bias was assessed using the Newcastle-Ottawa Scale. RESULTS: Eight papers related to the military population were included in this review. Migraine was the most commonly reported headache sub-phenotype, with a prevalence ranging from 33 to 92%. Additionally, one military study identified tension-type headaches as the most prevalent headache phenotype. Although not the primary phenotype, one military cohort reported that approximately one-third of their cohort experienced trigeminal autonomic cephalalgias, which were associated with exposure to blast injuries and prior concussions. CONCLUSION: This systematic review demonstrated that PTH in the military population frequently exhibit migraine-like features. Tension-type headache and trigeminal autonomic cephalalgias also occur, although less commonly reported. Sub-phenotyping PTH may be important for initiating effective treatment since different phenotypes may respond differently to medications. The study populations analyzed in this systematic review display heterogeneity, underscoring the necessity for additional research features, more stringent criteria and comprehensive recording of baseline characteristics. Characterizing headaches following injury is crucial for an accurate diagnosis to enable effective management and rehabilitation planning for our armed forces.

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 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)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0100.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.142
GPT teacher head0.428
Teacher spread0.286 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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