Attention Deficits After Aneurysmal Subarachnoid Hemorrhage Measured Using the Test of Variables of Attention
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The aim of this pilot study was to assess attention deficits in patients with aneurysmal subarachnoid hemorrhage using the test of variables of attention (TOVA). This is a computer-based continuous performance test providing objective measures of attention. We also compared the TOVA results with the attention and concentration domains of Montgomery Åsberg Depression Rating Scale and Montreal cognitive assessment, 2 examiner-administrated neuropsychological instruments. METHODS: Nineteen patients with moderate to good recovery (Glasgow outcome scale, 4-5) were assessed using the TOVA, Montgomery Åsberg Depression Rating Scale, and Montreal cognitive assessment. The measurements were done when the patients visited the hospital for a routine magnetic resonance imaging control of the aneurysm. RESULTS: TOVA performance was pathological in 58%. The dominating pattern was a worsening of performance in the second half of the test, commonly a failing to react to correct stimuli. We found no correlation between TOVA and the performance in concentration and attention domains of Montgomery Åsberg Depression Rating Scale and Montreal cognitive assessment. CONCLUSIONS: Attention deficits, measured by the TOVA, were common after subarachnoid hemorrhage. This should be further studied to improve outcome.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".