Quality of life in patients with primary and metastatic brain cancer as reported in the literature using the EORTC QLQ-BN20 and QLQ-C30
Bibliographic record
Abstract
The objective of this study is to compare the differences in quality of life (QoL) as assessed by the QLQ-BN20 and QLQ-C30 in patients with primary and metastatic brain neoplasms. A systematic literature search was conducted over the OvidSP platform in MEDLINE (1980-2012) and EMBASE (1980-2012). Studies in which the QLQ-BN20 was used as a QoL assessment for patients with malignant brain tumors (either metastatic or primary) were included in the study. Articles were included if they reported scores of at least one subscale of the QLQ-C30 or QLQ-BN20. The weighted means of the QLQ-BN20 and QLQ-C30 subscales were calculated based on sample size for included studies. Weighted analysis of variance was conducted to compare these scores in primary and metastatic brain patients. A p-value of < 0.05 was considered statistically significant. A total of 14 studies (16 arms: three brain metastases and 13 primary brain tumors) were identified and included in the data analysis. Fifteen of the 16 arms included QLQ-C30 scores along with QLQ-BN20 scores. Performance status of patients in both cohorts was similar. Patients with primary brain tumors and brain metastases had the following findings: physical functioning (weighted mean: 79.18 vs 74.93), global QoL (61.88 vs 59.44), role functioning (67.37 vs 75.00) and emotional functioning (70.44 vs 71.86); but none of them were statistically significantly different. Only cognitive functioning from the QLQ-C30 was significantly worse in patients with primary brain tumors (p-value = 0.0199). Despite cognitive function being significantly worse in patients with primary brain tumors, patients with metastatic brain tumors and patients with primary brain tumors have very similar QoL profiles. The study is limited by the large discrepancy in cohort sizes (1260 patients with primary brain cancer vs 183 patients with brain metastases) and the lack of clinical data.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".