Measuring cognitive assessment and intervention burden in patients with acquired brain injured: Development of the ”How Much is Too Much” questionnaire
Bibliographic record
Abstract
OBJECTIVE: To design and preliminarily test a questionnaire intended to measure patient treatment burden resulting from participation in cognitive assessments and interventions. METHODS: An expert consensus process was used to develop the concept of patient treatment burden and to determine the first set of questionnaire items and administration protocol. The pilot questionnaire was administered to 20 patients with mild to severe acquired brain injuries on completion of a 2-h or longer neuropsychological assessment. Following preliminary testing, the questionnaire was revised and re-evaluated by a second expert panel and content validity was assessed. RESULTS: Burden was defined as psychologically and/or physically aversive symptoms in response to cognitive assessment or intervention. The first questionnaire contained 21 items assigned to 3 categories: physical, cognitive, and emotional. Eighty-five percent of patients endorsed symptom level increases, with "tired/fatigued" the most frequently endorsed item (80% of patients). Instructions and test items were easily understood, and the questionnaire was quick to administer. Content validity ratio (CVR) of the revised questionnaire yielded 23 acceptable items and a subset met the highest CVR threshold (>0.78). CONCLUSION: This patient-reported outcome will ultimately help patients give voice to aversive experiences, and help clinicians and researchers to monitor and adapt assessments/treatments appropriately. Future steps in development are described.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".