Prospective evaluation of legal difficulties and quality of life in adult survivors of childhood cancer
Bibliographic record
Abstract
BACKGROUND: Adult survivors of childhood cancer (ASCC), especially those of the central nervous system (CNS), have increased risks of educational and social difficulties. It is therefore hypothesized they are more likely to encounter legal difficulties (LDs), such as workplace discrimination and disability insurance denials, which may negatively affect their quality of life (QoL). PROCEDURE: We developed a survey to collect information on patients' legal needs. QoL was assessed using the Functional Assessment of Cancer Therapy (FACT). RESULTS: We prospectively approached 112 ASCC, 111 (99.1%) of whom completed the survey. The median age of respondents was 7 years at diagnosis and 31 years at survey completion. CNS tumors were the most common malignancy (32.4%). LDs were common overall (40.7%), though more prevalent in patients with CNS versus non-CNS tumors (58.6% vs. 32.3%; P = 0.023). The most prevalent LD was workplace discrimination (58.3%). On multivariate analysis, CNS tumor was the only variable significantly associated with LDs (OR = 4.49, P = 0.041). Individuals with LDs had lower QoL scores compared to those without LDs (79.96 versus 91.83 on the FACT; P = 0.005). On multivariate analysis, individuals with LDs had lower QoL scores (14.95 points lower on the FACT), which is both clinically and statistically significant (P = 0.047). CONCLUSIONS: Legal difficulties are common in adult survivors of childhood cancer, especially those with brain tumors. Furthermore, individuals with legal difficulties have worse quality of life. Research is needed to develop effective and accessible legal resource programs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".