Health Status Measurements at Diagnosis As Predictors of Survival Among Adults With Brain Tumors
Bibliographic record
Abstract
PURPOSE: The intent of this study was to determine whether baseline measures of functional capacity and performance could be used to predict survival in adults following the diagnosis of brain tumors. PATIENTS AND METHODS: Comprehensive health status and health-related quality of life (HRQL) were measured using the Health Utilities Index (HUI; McMaster University, Hamilton, Canada) system by a self-assessment questionnaire in a survey of 100 consecutive patients. The Karnofsky Performance Score (KPS) and Folstein's Mini-Mental State Examination (MMSE) scores were measured by a physician blinded to the HUI results. The patients were observed for up to 5 years to recorded dates of death. RESULTS: An HUI questionnaire was completed for 93% of the patients and 69% died within 5 years of assessment. The HUI revealed a burden of morbidity and complexity of disability that far exceeded that reported for the general population. KPS and MMSE correlated strongly with each other (r = 0.52; P < .001). A decrease of 0.1 units in HUI Mark 2 (HUI2) self-care single-attribute utility score was associated with an increased hazard of death of 30% (P = .023) for patients with low-grade tumors (n=25). For patients with high-grade tumors (n=56), a 10 unit decrease in the KPS, a 5 unit decrease in MMSE, and a 0.1 decrease in HUI Mark 3 (HUI3) speech and dexterity single-attribute scores were associated with an increased hazard of death of 20% (P = .022), 26% (P = .015), 36% (P = .021), and 18% (P = .035), respectively. CONCLUSION: Scores derived from the measurement of HRQL following diagnosis can predict survival in adults with brain 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".