From multidimensional neuropsychological outcomes to a cognitive complication rate: The International Subarachnoid Aneurysm Trial
Bibliographic record
Abstract
BACKGROUND: The neuropsychological arm of the International Subarachnoid Aneurysm Trial (N-ISAT) evaluated the cognitive outcome of 573 patients at 12 months following subarachnoid haemorrhage (SAH). The assessment included 29 psychometric measures, yielding a substantial and complex body of data. We have explored alternative and optimal methodologies for analysing and summarising these data to enable the estimation of a cognitive complication rate (CCR). Any differences in cognitive outcome between the two arms of the trial are not however reported here. METHODS: All individual test scores were transformed into z-scores and a 5th percentile cut-off for impairment was established. A principal components analysis (PCA) was applied to these data to mathematically transform correlated test scores into a smaller number of uncorrelated principal components, or cognitive 'domains'. These domains formed the basis for grouping and weighting individual patients' impaired scores on individual measures. In order to increase the sample size, a series of methods for handling missing data were applied. RESULTS: We estimated a 34.1% CCR in all those patients seen face-to-face, rising to 37.4% CCR with the inclusion of patients who were unable to attend assessment for reason related to the index SAH. This group demonstrated significantly more self and carer/relative rated disability on a Health Related Quality of Life questionnaire, than patients classified as having no functionally significant cognitive deficits. CONCLUSION: Evaluating neuropsychological outcome in a large RCT involves unique methodological and organizational challenges. We have demonstrated how these problems may be addressed by re-classifying interval data from 29 measures into a dichotomous CCR. We have presented a 'sliding scale' of undifferentiated individual cognitive impairments, and then on the basis of PCA-derived cognitive 'domains', included consideration of the distribution of impairments in these terms. In order to maximize sample size we have suggested ways for patients who did not complete the entire protocol to be included in the overall CCR. ISAT TRIAL REGISTRATION: ISRCTN49866681.
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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 | 0.055 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".