A sample of headache incidence in post concussive patients
Bibliographic record
Abstract
Objective Headache is one of the most common and debilitating symptoms after traumatic brain injury (TBI) and may persist months to years after the initial injury. The purpose of this pilot project was to determine the incidence and characteristics of headaches within the TBI population. Design Descriptive study. Setting Data were collected from an out-patient clinic specialising in the management of TBI. Participants 42 patients (24 male, 18 female) with headache symptoms. Intervention Treating physicians collected data on only one appointment for each patient. Of the 160 patients visiting the clinic in May 2012, 30% complained of headaches. Headache symptoms were defined by the International Headache Society Classification ICHD-II criteria. Outcome Measures Measures relating to gender, time since head injury, type of headache, severity of headache, frequency of headaches, location of headache and current medication were recorded. Results Fifty-seven percent of the population was male. Almost 40% of patients had sustained a head injury over a year prior to data collection. Half of the subjects suffered from tension-type headaches, 14% of patients from migraines, and 7% from both migraine and tension-type headaches. One third reported headaches to occur on a daily basis and 81% percent suffered from headaches that were at least moderate in severity. Thirty-six percent of patients were taking either Advil or Tylenol at the time of data collection. Conclusions The pilot data confirmed clinical belief of the necessity to conduct large-scale trials to implement and evaluate interventions for the management of headaches in the TBI population. Competing interests None.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".