Education, employment, insurance, and marital status among 694 survivors of pediatric lower extremity bone tumors
Bibliographic record
Abstract
BACKGROUND: With increasing numbers of childhood cancer survivors, direct sequelae of cancer therapy and psychosocial outcomes are becoming more important. The authors described psychosocial outcomes (education, employment, health insurance, and marriage) for survivors of pediatric lower extremity bone tumors. METHODS: The long-term follow-up study of the Childhood Cancer Survivor Study is a multiinstitutional cohort study comprising 14,054 individuals who have survived for 5 or more years after treatment for cancer diagnosed during childhood or adolescence. Baseline demographic and medical information were obtained. Six hundred ninety-four survivors had osteosarcoma or Ewing sarcoma of the lower extremity or pelvis and were classified by amputation status and by age at diagnosis. The median age at diagnosis was 14 years old with a median of 16 years of follow up since diagnosis. Demographic characteristics were used to analyze the rates of psychosocial outcomes. RESULTS: Amputation status and age at diagnosis did not significantly influence any of the measured psychosocial outcomes. Education was a significant positive predictor of employment, having health insurance, and being currently in their first marriage. Male gender predicted ever being employed and female gender predicted having health insurance and marriage. When compared with siblings, amputees had significant deficits in education, employment, and health insurance. CONCLUSIONS: Overall, no differences between amputees and nonamputees were found. However, gender and education play a prominent role. When compared with siblings, amputees in this cohort may benefit from additional supports.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".