Early parental knowledge of late effect risks in children with cancer
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Despite the pervasiveness of late effects in childhood cancer survivors, many parents feel inadequately informed about their child's risks. We assessed early parental knowledge of risks of late effects and predictors of increased knowledge. DESIGN/METHODS: Parents of children receiving cancer treatment at Dana-Farber/Boston Children's Cancer and Blood Disorders Center were surveyed about their knowledge of their child's likelihood of eight late effects. Individual risk for each late effect (yes/no) was assessed using the Children's Oncology Group's Long-Term Follow-Up Guidelines v5 as a reference. Descriptive statistics were used to summarize knowledge scores; ordinal logistic regression was used to identify predictors of higher knowledge. RESULTS: Of 96 parent participants, 11 (11.46%) correctly identified all of their child's risks for the eight late effects. Five of eight was the median number of correctly identified late effect risks. Among 21 parents whose children were at risk for ototoxicity, 95% correctly identified this risk. Conversely, parents of at-risk children were less knowledgeable about risks of secondary malignancy (63% correct identification, of N = 94 at risk), cardiac toxicity (61%; N = 71), neurocognitive impairment (56%; N = 63), and infertility (28%; N = 61). Ordinal logistic regression analysis identified no significant differences in parental knowledge of late effect risks by any factors evaluated. CONCLUSIONS: Gaps in parental knowledge of potential late effects of childhood cancer treatment emerge early in a child's care, and parents are more knowledgeable about some late effects, such as ototoxicity, than others, such as infertility. As no child- or parent-specific factors were associated with increased knowledge of late effect risks, interventions must be applied broadly.
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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 | 0.009 |
| 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 | 0.001 |
| 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".