Development of a symptom expectation questionnaire for minor head injury
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Expectations and beliefs are important predictors of outcome following minor head injury. In this paper, the primary purpose is to develop a simple symptom expectation questionnaire for minor head injury for use in future research studies. METHODS: An existing database of 179 injury-naive subjects who completed a 56-item checklist of expected symptoms for minor head injury was analyzed to determine which items could correctly identify an a priori case definition of an expecter (a subject who expected at least one of these symptoms would remain chronic following minor head injury). A total of six of the 56 items were found to be discriminatory, and these were tested in additional subject groups against the original questionnaire. RESULTS: From the original database of 179 subjects completing a 56-item symptom expectation checklist, 135 expected that at least one of the 56 symptoms would be chronic following minor head injury. The 135 expecters, however, all chose at least one of six items: headache, anxious or worried, depressed, difficulty concentrating, dizziness, and neck pain. Using these six items, in two new groups of subjects, all those who endorsed one of the 56 symptoms as likely to be chronic following minor head injury (expecters) could also be identified on the 6-item checklist. CONCLUSIONS: A shortened (6-item) symptom expectation checklist of commonly reported symptoms following minor head injury (headache, anxious or worried, depressed, difficulty concentrating, dizziness, and neck pain) correctly identifies subjects who expect that at least one symptom will be chronic following minor head injury (i.e., an expecter).
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".