Recommendations for the surveillance of education and employment outcomes in survivors of childhood, adolescent, and young adult cancer: A report from the International Late Effects of Childhood Cancer Guideline Harmonization Group
Bibliographic record
Abstract
Educational achievement and employment outcomes are critical indicators of quality of life in survivors of childhood, adolescent, and young adult (CAYA) cancer. This review is aimed at providing an evidence-based clinical practice guideline (CPG) with internationally harmonized recommendations for the surveillance of education and employment outcomes in survivors of CAYA cancer diagnosed before the age of 30 years. The CPG was developed by a multidisciplinary panel under the umbrella of the International Late Effects of Childhood Cancer Guideline Harmonization Group. After evaluating concordances and discordances of 4 existing CPGs, the authors performed a systematic literature search through February 2021. They screened articles for eligibility, assessed quality, and extracted and summarized the data from included articles. The authors formulated recommendations based on the evidence and clinical judgment. There were 3930 articles identified, and 83 of them, originating from 17 countries, were included. On a group level, survivors were more likely to have lower educational achievement and more likely to be unemployed than comparisons. Key risk factors for poor outcomes included receiving a primary diagnosis of a central nervous system tumor and experiencing late effects. The authors recommend that health care providers be aware of the risk of educational and employment problems, implement regular surveillance, and refer survivors to specialists if problems are identified. In conclusion, this review presents a harmonized CPG that aims to facilitate evidence-based care, positively influence education and employment outcomes, and ultimately minimize the burden of disease and treatment-related late adverse effects for survivors of CAYA cancers. LAY SUMMARY: A multidisciplinary panel has developed guidelines for the surveillance of education and employment outcomes among survivors of childhood, adolescent, and young adult cancer. On the basis of evidence showing that survivors are at risk for lower educational achievement and unemployment, it is recommended that all survivors receive regular screening for educational and employment outcomes.
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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 | 0.052 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".