Effect of hemorrhagic cerebrospinal fluid drainage on cognitive function after intracranial aneurysm clipping
Bibliographic record
Abstract
Objective: The effects of repeated lumbar puncture and continuous lumbar cistern drainage on the cognitive function of patients with aneurysmal subarachnoid hemorrhage were compared and analyzed. Methods: Retrospective analysis was performed on 59 patients with aneurysmal subarachnoid hemorrhage treated at our Neurosurgery Department between October 2017 and October 2018. According to the hemorrhagic cerebrospinal fluid drainage mode after aneurysm clipping, the patients were divided into the following two groups: the repeated lumbar puncture drainage (Group A, n = 28) and continuous lumbar cistern drainage (Group B, n = 31). Before and 1 month after surgery, the cognitive function of the patients was scored using the Montreal Cognitive Assessment Scale. Scores of 27~30 were defined as normal, and scores of < 27 as cognitive impairment. Results: The incidences of cognitive impairment were 46% (13/28) and 32% (10/31) for Groups A and B, respectively, before surgery, but the difference was not significant ( P > 0.05). The incidences of cognitive impairment were 35% (10/28) and 12% (4/31) for Groups A and B, respectively, at 1 month after surgery, with significant difference ( P < 0.05). Conclusion: Compared with repeated lumbar puncture, continuous lumbar cistern drainage for aneurysmal subarachnoid hemorrhage significantly reduced the incidence of cognitive impairment after aneurysm clipping.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".