Self-reported symptoms during post-mild traumatic brain injury in acute phase: Influence of interviewing method
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To examine the role played by two interviewing methods used in the evaluation of acute self-reported symptoms after a mild traumatic brain injury (mTBI). METHOD AND PROCEDURES: A cohort of 354 mTBI-adult participants was recruited over a period of 36 months in emergency rooms of five Canadian trauma hospitals. Participants were contacted by phone at the first and fourth week and at the third month following their mTBI in order to evaluate their symptoms. First, with the 'spontaneous' method, they freely identified those symptoms. Secondly, with the 'suggested' method, they identified their symptoms according to a standardized checklist made-up of the most common post-mTBI symptoms (Rivermead Post-Concussion Symptoms Questionnaire). RESULTS: Globally, results showed a significant interaction effect between the interviewing method and measurement times of the symptoms. Participants reported significantly more symptoms with the suggested method compared to the spontaneous method for each measurement time. On the other hand, they reported significantly fewer symptoms from the first week to the third month of evaluation for either interviewing method. Moreover, the types of symptoms reported for both interviewing methods were different. CONCLUSION: The interviewing method used influences number and type of self-reported symptoms during the first 3 months post-mTBI.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".