Documentation of Late-Effects Risks and Screening Recommendations for Adolescent and Young Adult Central Nervous System, Soft Tissue, or Bone Tumor Survivors Treated with Radiotherapy in British Columbia, Canada
Bibliographic record
Abstract
Purpose: To assess the documentation of late-effects (LE) risks and screening recommendations in medical records of adolescent and young adult central nervous system (CNS), soft tissue, and bone tumor survivors. Methods: The medical records of all patients diagnosed with a CNS neoplasm, an arteriovenous malformation, a soft tissue, and bone tumor, at ages 15–39 years, treated between 1985 and 2010 with radiation therapy in the province of British Columbia, Canada, surviving >5 years, alive, and discharged to the community were assessed. The documentation of LE risks and screening recommendations were analyzed descriptively. Results: In the medical records of 132 CNS tumor survivors and 94 soft tissue or bone tumor survivors, 15% and 13% included no documentation of LE risks, 21% and 22% included only nonspecific documentation, and 64% and 65% minimal documentation, respectively. Documentation of at least one specific LE risk increased significantly among CNS tumor patient charts (from 29% in 1980–1989, to 67% in 1990–1999, to 88% in 2000–2010, χ 2 [2, N = 132] = 32.257, p < 0.000) and soft tissue or bone tumor patient charts (from 47% [1980–1989] to 56% [1990–1999] to 78% [2000–2010], χ 2 [2, N = 94] = 6.702, p = 0.035). There was no documentation of a screening recommendation in 75% of CNS tumor patient charts and 91% of soft tissue and bone tumor charts. Conclusion: The documentation of LE risks and screening recommendations has been limited, highlighting the need to improve written communication with primary care providers.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".