Prospective study on the presence of cognitive impairments in patients with brain tumors
Bibliographic record
Abstract
Objective: The purpose of our study was to evaluate the cognitive decline in patients with primitive brain tumors. Material and methods: We enrolled in our study 188 patients diagnosed with brain tumors, hospitalized in the Clinic of Neurology Craiova between January 2006 and December 2010. Depending on the origin of the brain tumors the group was divided as follows: • Group A, composed of 45 patients with tumors of the meninges • Group B, composed of 105 patients with neuroepithelial tumors Each patient was evaluated by neurological and neuroimaging exam (computed tomography and/or nuclear magnetic resonance). For the evaluation of global disability we used Karnofsky Performance Status Scale. Cognitive function was assessed using: Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). The results were analyzed by Student’s T test, considering statistically significant p<0,05. Results: Statistical analysis, in terms of the average age and average years of education didn’t show significant differences between the two groups. Evaluation of the global functionality, using Karnofsky score, did not show significant differences between the two groups. On MMSE group A obtained an average score of 26,7 points, while in group B, the average score was 25,6 points. On MoCA scale there was, also, a statistically significant difference between the two groups; group A obtained an average score of 23,3 points and group B an average score of 20,3 points. Conclusion: Our study demonstrated a statistically significant cognitive decline in patients with neuroepithelial tumors compared with patients diagnosed with tumors of the meninges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".