A questionnaire based review of long‐term follow‐up programs for survivors of childhood cancer in Canada
Bibliographic record
Abstract
BACKGROUND: Major advances have been made in the treatment of childhood cancer; however, survivors of childhood cancer are at increased risk for morbidity and mortality. There is little literature regarding available long-term follow-up programs for survivors of childhood cancer. PROCEDURE: In March 2007, 16 surveys were sent to pediatric hematology/oncology programs across Canada to determine what programs were available for survivors of childhood cancer, and the nature of such programs. RESULTS: Of 15 participating centers, 13 (87%) have multi-disciplinary programs for the long-term follow-up of pediatric cancer survivors. Research databases were documented in 9/15 (60%) of centers to document late effects. Dedicated programs for adult survivors of childhood cancer were established in 8/15 (53%) of centers. Access to subspecialty care for survivors was rated as quite good. Concerns were raised by many participants about patients being lost to follow-up. Respondents indicated that primary care physicians appear to be under-represented within dedicated long-term follow-up programs. CONCLUSION: Long-term follow-up programs for survivors of childhood cancer are available in 87% of Canadian pediatric oncology centers. While programs reported good access to care for childhood survivors, many adult survivors of childhood cancer have more limited timely access to services and patients are often lost to follow-up. New models of care incorporating primary care physicians are necessary due to growing numbers of survivors.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".