Study of cognitive dysfunction before and after operation in patients with cerebral tumors
Bibliographic record
Abstract
Objectives To explore the features of cognitive dysfunction of the patients with cerebral tumors and effect of surgery on it.Methods The preoperative and postoperative cognitive function were assessed by the Montreal Cognitive Assessment (MoCA) and statistically analyzed in 100 patients with cerebral tumors.Results The incidences of cognitive dysfunction in the patients with cerebral tumors were 48.0% and 62.0% respectively before and after surgery.The incidence of the cognitive dysfunction was significantly higher after the operation than that before the operation (P0.05).The incidence (61.7%,37/60) of cognitive dysfunction in the patients with frontal and/or temporal tumors was significantly higher than that (30.0%,12/40) in patients with the non-frontal and non-temporal tumors (P0.05).And the visuospatial and executive,memory,attention and language functions impairment in the patients with frontal and/or temporal tumors were significantly more severe than that in the patients with non-frontal and non-temporal tumors (P0.01).The incidence (70.3%,26/37) of cognitive dysfunction in the patients with left hemisphere tumors was insignificantly higher than that (53.5%,23/43) in the patients with right hemisphere tumors (P0.05).Conclusions There is cognitive dysfunction in the patients with cerebral tumors,in whom,the cognitive dysfunction may be worsened by surgery.MoCA is a sensitive method to assess the cognitive dysfunction in the patients with cerebral tumors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".