Effect of Aneurysmal Subarachnoid Hemorrhage on Word Generation
Bibliographic record
Abstract
BACKGROUND: Aneurysmal subarachnoid hemorrhage (aSAH) survivors commonly exhibit impairment on phonemic and semantic fluency tests; however, it is unclear which of the contributing cognitive processes are compromised in aSAH patients. One method of disentangling these processes is to compare initial word production, which is a rapid, semiautomatic, frontal-executive process, and late phase word production, which is dependent on more effortful retrieval and lexical size and requires a more distributed neural network. METHODS: Seventy-two individuals with aSAH and twenty-five control subjects were tested on a cognitive battery including the phonemic and semantic fluency task. Demographic and clinical information was also collected. RESULTS: Compared to control subjects, patients with aSAH were treated by clipping and those with multiple aneurysms were impaired across the duration of the phonemic test. Among patients treated by coiling, those with anterior communicating artery aneurysms or a neurological complication (intraventricular hemorrhage, vasospasm, and edema) showed worse output only in the last 45 seconds of the phonemic test. Patients performed comparably to control subjects on the semantic test. CONCLUSIONS: These results support a "diffuse damage" hypothesis of aSAH, indicated by late phase phonemic fluency impairment. Overall, the phonemic and semantic tests represent a viable, rapid clinical screening tool in the postoperative assessment of patients with aSAH.
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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 | 0.000 | 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 | 0.000 |
| 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".