Neuropsychological detection of cognitive impairment: Inter-rater agreement and factors affecting clinical decision-making
Bibliographic record
Abstract
The agreement between neuropsychologists identifying cognitive impairment (CI) in older adults was examined, as were factors influencing the classification process. Twenty four neuropsychologists in 18 study centers classified cases with or without CI after reviewing neuropsychological findings and other relevant information. All cases were participants in the third wave of the Canadian Study of Health and Aging, a study of CI in later life. For 117 randomly selected cases, a second neuropsychologist reviewed the same material and reclassified the cases. Cases given the same (concordant) or different (discordant) classifications were compared with respect to patient and rater characteristics. The inter-rater agreement was moderate (77.7% agreement, kappa = .49). On all measures of cognitive functioning, the concordant group without impairment obtained a higher mean score than the discordant group, and the discordant group obtained a higher mean score than the concordant group with impairment. For 5 out of 8 cognitive measures, the concordant group with impairment differed from the concordant group without impairment and the discordant group, but the latter two groups did not differ significantly. The findings are comparable to others in the field and highlight the need for neuropsychologists to further clarify procedures for identifying subtle, or mild, forms of cognitive impairment.
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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 | 0.160 | 0.274 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".