Screening tools for early neuropsychological impairment after aneurysmal subarachnoid hemorrhage
Bibliographic record
Abstract
BACKGROUND: Although most aneurysmal subarachnoid hemorrhage (aSAH) patients suffer from neuropsychological disabilities, outcome estimation is commonly based only on functional disability scales such as the modified Rankin Scale (mRS). Moreover, early neuropsychological screening tools are not used routinely. OBJECTIVE: To study whether two simple neuropsychological screening tools identify neuropsychological deficits (NPDs), among aSAH patients categorized with favorable outcome (mRS 0-2) at discharge. METHODS: We reviewed 170 consecutive aSAH patients that were registered in a prospective institutional database. We included all patients graded by the mRS at discharge, and who had additionally been evaluated by a neuropsychologist and/or occupational therapist using the Montreal Cognitive Assessment (MoCA) and/or Rapid Evaluation of Cognitive Function (ERFC). The proportion of patients with scores indicative of NPDs in each test were reported, and spearman correlation tests calculated the coefficients between the both neuropsychological test results and the mRS. RESULTS: Of the 42 patients (24.7%) that were evaluated by at least one neuropsychological test, 34 (81.0%) were rated mRS 0-2 at discharge. Among these 34 patients, NPDs were identified in 14 (53.9%) according to the MoCA and 8 (66.7%) according to the ERFC. The mRS score was not correlated with the performance in the MoCA or ERFC. CONCLUSION: The two screening tools implemented here frequently identified NPDs among aSAH patients that were categorized with favorable outcome according to the mRS. Our results suggest that MoCA or ERFC could be used to screen early NPDs in favorable outcome patients, who in turn might benefit from early neuropsychological rehabilitation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".