Free Recall Memory Performance after Aneurysmal Subarachnoid Hemorrhage
Bibliographic record
Abstract
Memory deficits for survivors of aneurysmal subarachnoid hemorrhage (SAH) are common, however, the nature of these deficits is not well understood. In this study, 24 patients with SAH and matched control participants were asked to study six lists containing words from four different categories. For half the lists, the categories were presented together (organized lists). For the remaining lists, the related words were presented randomly to maximize the use of executive processes such as strategy and organization (unorganized lists). Across adjoining lists, there was overlap in the types of categories given, done to promote intrusions. Compared to control participants, SAH patients recalled a similar number of words for the organized lists, but significantly fewer words for the unorganized lists. SAH patients also reported more intrusions than their matched counterparts. Separating patients into anterior communicating artery ruptures (ACoA) and ruptures in other regions, there was a recall deficit only for the unorganized list for those with ACoA ruptures and deficits across both list types for other rupture locations. These results suggest that memory impairment following SAH is likely driven by impairment in the executive components of memory, particularly for those with ACoA ruptures. Such findings may help direct future cognitive-therapeutic programs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".