Systematic review and meta‐analysis of health‐related quality of life in pediatric CNS tumor survivors
Bibliographic record
Abstract
Abstract Background Pediatric central nervous system (CNS) tumor survivors are at high risk for numerous late effects including decreased health‐related quality of life (HRQOL). Our objective was to summarize studies describing HRQOL in pediatric CNS tumor survivors and compare HRQOL outcomes in studies that included a comparison group. Procedure EMBASE, MEDLINE, and PsychINFO were used to identify relevant articles published until August, 2016. Eligible studies reported outcomes for pediatric CNS tumor survivors diagnosed before age 21, at least 5 years from diagnosis and/or 2 years off therapy and used a standardized measure of HRQOL. All data were abstracted by two reviewers. Random‐effects meta‐analyses were performed using Review Manager 5.0. Results Of 1,912 unique articles identified, 74 were included in this review. Papers described 29 different HRQOL tools. Meta‐analyses compared pediatric CNS tumor survivors to healthy comparisons and other pediatric cancer survivors separately. HRQOL was significantly lower for CNS (n = 797) than healthy comparisons (n = 1,397) (mean difference = –0.54, 95% confidence interval [CI] = –0.72 to –0.35, P < 0.001, I 2 = 35%). HRQOL was also significantly lower for CNS (n = 244) than non‐CNS survivors (n = 414) (mean difference = –0.56, 95% CI = –0.73 to –0.38, P < 0.00001, I 2 = 0%). Conclusions Pediatric CNS tumor survivors experience worse HRQOL than healthy comparisons and non‐CNS cancer survivors. Future HRQOL work should be longitudinal, and/or multisite studies that examine HRQOL by diagnosis and treatment modalities.
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".