P.127 Quality of life (QoL) using EORTC QoL-C30 and BN20 among patients who underwent brain tumor resection in a tertiary hospital in Saudi Arabia
Bibliographic record
Abstract
Background: Quality of life (QoL) is the awareness of individuals’ well-being in life in physical, personal, mental and social wellbeing and needs to be addressed in brain tumor patients. Methods: A retrospective study conducted in 2017 in a single academic center that included patients diagnosed with brain tumors in a 10 year period. The assessment of the QoL was done using the European Organization for Research and Treatment of Cancer (EORTC), a standardized model (QLQ-C30) that assess several domains (Global Health, Physical function, Role functioning, Emotional Functioning, Cognitive functioning, social functioning and symptoms domain) and Brain cancer model (BN20) to assess symptoms to evaluate all aspects of wellbeing. Results: The total number of patients included in this study is 76 patients with no gender predilection. The most common brain tumor was meningioma by 40% followed by glioma/ others. More than half of the brain tumor patients had a WHO grade I (65%), intermediate grading grade II (15%) and higher grading grade III/IV (20%). The scales and measurements of functioning in life were low in all types of brain tumors. Conclusions: Quality of life in brain tumor patients seemed poor regardless of the type. Further prospective studies are needed to assess QoL worldwide.
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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 | 0.000 | 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 | 0.000 |
| 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".