Effects of “Diagnosis Threat” on Cognitive and Affective Functioning Long After Mild Head Injury
Bibliographic record
Abstract
Persistent cognitive complaints are common following a mild head injury (MHI), but deficits are rarely detected on neuropsychological tests. Our objective was to examine the effect of symptom expectation on self-report and cognitive performance measures in MHI individuals. Prior research suggests that when MHI participants are informed they may experience cognitive difficulties, they perform worse on neuropsychological tests compared to MHI participants who are uninformed. In this study, undergraduate students with and without a prior MHI were either informed that the study's purpose was to investigate the effects of MHI on cognitive functioning ("diagnosis threat" condition) or merely informed that their cognitive functioning was being examined, with no mention of status ("neutral" condition). "Diagnosis threat" MHIs self-reported more attention failures compared to "diagnosis threat" controls and "neutral" MHIs, and more memory failures compared to "diagnosis threat" controls. In the "neutral" condition, MHIs reported higher anxiety levels compared to controls and compared to "diagnosis threat" MHIs. Regardless of condition, MHIs performed worse on only one neuropsychological test of attention span. "Diagnosis threat" may contribute to the prevalence and persistence of cognitive complaints made by MHI individuals found in the literature, but may not have as strong of an effect on neuropsychological measures.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".