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Record W2883841256 · doi:10.1097/htr.0000000000000417

Traumatic Brain Injury Following Military Deployment: Evaluation of Diagnosis and Cause of Injury

2018· article· en· W2883841256 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2018
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsSoftware deploymentMilitary deploymentService memberTraumatic brain injuryMedicineActive dutyMilitary medicineMilitary personnelPoison controlMilitary serviceInjury preventionEmergency medicineMedical emergencyPsychiatryEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the prevalence of delayed traumatic brain injury (TBI) diagnosis and cause of injury that resulted in a TBI diagnosis after military deployment. DESIGN: Medical record notes were reviewed in 2016 from a random sample of 1150 US military service members who had their first-time deployment in 2011 and likely sustained a TBI. Location and cause of the injury were extracted from the progress note for analysis. PARTICIPANTS AND SETTING: Active-duty US military service members who received an International Classification of Diseases, Ninth Revision code for a TBI diagnosis in a military facility. MAIN OUTCOME MEASURES: Presence of TBI, location of injury, cause of injury, and time of diagnosis with respect to deployment. RESULTS: The odds of being diagnosed with a deployment-related TBI were 8 times higher during the first 4 weeks upon return from deployment than the subsequent 32 weeks. The likelihood of diagnosing a deployment-sustained TBI during weeks 5 to 32 was 2 times higher than during 33 to 76 weeks following return from deployment. The proportion of deployment-related TBI diagnoses decreased with time following return from deployment but remained above 40% during weeks 33 to 76. Service branch, gender, race, occupation, and time between TBI diagnosis and return from deployment were significant predictors of deployment-related TBIs. Moving motor vehicle, sports, parachute, and being struck by objects were the top causes of injury in garrison (nondeployed setting), whereas blast produced the majority (66%) of all causes of injuries that resulted in a TBI in the deployed setting. CONCLUSION: The increased incidence rate of a TBI diagnosis following deployment can be attributed to delayed diagnosis of TBI sustained from injuries during deployment. TBIs sustained during deployment can be diagnosed beyond the initial 4 weeks after return from deployment and may continue up to 76 weeks following return from deployment.

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.

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)0.007
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.097
GPT teacher head0.427
Teacher spread0.329 · 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