Development of the Common Cognitive Complaints after Concussion (C4) questionnaire: a treatment-planning tool for military service members and veterans with mild traumatic brain injury
Bibliographic record
Abstract
Objective: To develop an activity-focused self-report tool to guide selection of treatment targets in cognitive rehabilitation for adults with mild traumatic brain injury (mTBI). Setting: Military and veteran treatment facilities. Participants: Twenty-one service members and 32 veterans with a history of mTBI; 25 veterans with orthopedic injury (OI). Design: Clinical tool development. Main measures: Common Cognitive Complaints after Concussion (C4) questionnaire. Results: We reviewed measures used in mTBI research or clinic, to identify items that could be used for selecting activity-level therapy targets as part of a treatment planning tool. To establish face and content validity, an initial item pool was reviewed by five speech-language pathology or occupational therapy mTBI experts who selected items relevant to their clinical practice, gave feedback on item wording, and suggested additional items. The result was a questionnaire with 22 activity-based items and one bias-check item. The C4 was then used in a feasibility mTBI treatment trial to identify treatment targets, and clinicians provided feedback on its utility. The C4 was also administered to an OI group to evaluate the distinctiveness of the items to mTBI symptoms. Conclusion: The C4 adequately captured activity-level functional impairments common to mTBI, and clinicians endorsed its utility as a useful tool to personalize treatment targets.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".