Childhood Cancer Survivors' Knowledge About Their Past Diagnosis and Treatment
Bibliographic record
Abstract
CONTEXT: Adult survivors of childhood cancer are at risk for adverse effects later in life but may have limited access to information about their diagnosis and treatment. This knowledge is necessary to motivate them to seek medical follow-up and to report essential history to health care professionals. OBJECTIVE: To assess knowledge of adult survivors of childhood cancer about their primary cancer diagnosis and associated therapies. DESIGN, SETTING, AND PARTICIPANTS: Cross-sectional survey of 635 consecutive survivors (approximately 5%) drawn from 12 156 participants 18 years or older participating in the Childhood Cancer Survivor Study (a multiinstitutional cohort of individuals diagnosed between January 1, 1970, and December 31,1986, at an age <21 years, who had survived 5 years from diagnosis). The survey assessed knowledge of their cancer diagnosis and associated therapies in a 3- to 5-minute telephone questionnaire. MAIN OUTCOME MEASURES: Responses were compared with medical record data for accuracy, sensitivity, specificity, and positive and negative predictive value. RESULTS: Overall, 72% accurately reported their diagnosis with precision and 19% were accurate but not precise. Individuals with central nervous system (CNS) cancer (odds ratio, 5.1; 95% confidence interval, 2.6-9.9) and neuroblastoma (OR, 4.2; 95% CI, 1.8-9.6) were more likely not to know their cancer diagnosis. Participants' accuracy rates for reporting their treatment history was 94% for chemotherapy, 89% for radiation, and 93% for splenectomy. Among those who received anthracyclines, only 30% recalled receiving daunorubicin therapy and 52% recalled receiving doxorubicin therapy, even after prompting with the drugs' names. Among those who received radiotherapy, 70% recalled the site of radiotherapy. History of receiving a written medical summary, attending a long-term follow-up clinic, and anxiety about late effects were not associated with greater knowledge. CONCLUSIONS: Important knowledge deficits exist among adult survivors of childhood cancer regarding basic aspects of their diagnosis and treatment. Such deficits could impair survivors' ability to seek and receive appropriate long-term follow-up care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".