Characteristics of Patients Returning to Work After Brain Tumor Surgery
Bibliographic record
Abstract
Objective: To investigate the differences between patients returning to work and those who did not after brain tumor surgery. Methods: Patients were evaluated before surgery and after 3 months. The Montreal Cognitive Assessment test, Trail-Making Test (parts A and B), 15-word Rey–Osterrieth Word List (immediate and delayed recall), F-A-S tests, and Karnosfky Performance Status were used to assess cognitive status, attention, executive functions, memory, word fluency, and functional status. Patient-reported outcome measures (PROMs) used to evaluate emotional distress and disability were the Hospital Anxiety and Depression Scale and World Health Organization Disability Assessment Schedule. Clinical and work-related variables, PROMs, and cognitive tests were compared using chi-squared, t -test or Mann–Whitney U test. Results: Sixty patients were included. Patients returning to work were 61.3 and 31.0% among people with meningioma and glioma, respectively. They reported lower postoperative disability and lesser home-to-work travel time. Patients with meningioma also showed better preoperative and postoperative attention and executive functions, better postoperative functional and cognitive status, and lower frequency of treatments. Conclusions: These variables should be considered in a clinical context to plan interventions for people who need support during return to work and in future research to investigate preoperative and postoperative predictive factors of going back to work.
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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 | 0.000 |
| 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".