Educational attainment of childhood cancer survivors: A systematic review
Bibliographic record
Abstract
BACKGROUND: Advances in treatment mean that most children diagnosed with cancer during childhood survive. Therefore, it is increasingly important to examine the long-term consequences of childhood cancer, including educational attainment. This systematic review investigated whether the educational attainment of childhood cancer survivors differ from the cancer-free population. DESIGN/METHODS: We searched seven databases for articles published from January 2005 to August 2018. We identified full papers in English, reporting primary data on academic attainment of adult survivors of childhood cancer, compared to a control group. Quality appraisal was conducted using the Newcastle-Ottawa Scale. RESULTS: Fourteen studies met the inclusion criteria. Nine papers included patients with various types of cancers, four focused on a single type of cancer, and one on patients who underwent stem cell transplantation. Of the 14 papers, 2 studies were considered good quality, 10 were considered adequate quality, and 2 were considered poor quality. Four studies reported more favorable educational attainment among survivors while six did not report significant differences. Less favorable attainment was consistently reported for CNS survivors in four studies. CONCLUSION: The literature does not provide a clear pattern of the long-term consequences of childhood cancer on education attainment. While this may suggest that there is no consistent difference between the education attainment of cancer survivors and controls, it may also be the result of limitations in the existing research. To better assess the education attainment of survivors, there is a need for high-quality studies, with appropriate comparators, and standardized measures of education attainment across countries.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".