Component analysis of verbal fluency scores in severe traumatic brain injury
Bibliographic record
Abstract
PRIMARY OBJECTIVE: This study set out to examine the sensitivity of verbal fluency component scores in severe traumatic brain injury (TBI). RESEARCH DESIGN: A retrospective cross-sectional design was used, with control participants chosen at random from the community and TBI patients from litigation cases. METHODS AND PROCEDURES: Fifty-four healthy controls and 28 patients who had incurred a severe TBI were included in the study. The Controlled Oral Word Association test was rescored to include clustering and switching scores for phonemic and semantic fluency separately. The scores were compared between controls and TBI patients using independent samples t-tests. MAIN OUTCOMES AND RESULTS: The findings demonstrate that component scores for semantic fluency yielded the largest effect sizes overall (d = 1.32 and d = 1.53), but not phonemic fluency. Total words generated in phonemic fluency yielded the largest effect size, although still modest (d = 0.62). CONCLUSIONS: While verbal fluency may be a useful test tool to elicit evidence of neuropsychological impairment after TBI, these findings are consistent with previous research demonstrating that component scores are more sensitive indices. There is potential clinical utility in using component scores for examining the specific severity of verbal fluency impairment in TBI and guiding rehabilitation efforts.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".