Incremental contribution of reported previous head injury to the prediction of diagnosis and cognitive functioning in older adults
Bibliographic record
Abstract
BACKGROUND: Severe brain injuries may be a risk factor for the development of dementia in later life. Less severe incidents with relatively short or even no loss of consciousness may not carry the same prognosis. OBJECTIVES: This study used data from the first two waves of the Canadian Study of Health and Ageing (CSHA-1 and CSHA-2) to investigate two questions. (1) Does a history of head injury improve the prediction of the diagnosis of dementia? This analysis was based on the 921 elderly individuals who underwent a clinical assessment in CSHA-2 and, 5 years earlier, had reported whether or not they had had a head injury. (2) Does adding information about a history of head injury improve the prediction of neuropsychological test scores? This second analysis included 585 elderly people who underwent neuropsychological assessment in both waves and who also reported whether or not they had had a history of mild or moderate-to-severe head injury. RESULTS: RESULTS showed that the inclusion of head injury information did not improve the prediction of diagnostic outcome of dementia. Age and overall cognitive status were associated with most neuropsychological test scores, more so than the more limited influence of chronic health problems, which was associated with about half of the 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".