Parental optimism in poor prognosis pediatric cancers
Bibliographic record
Abstract
OBJECTIVE: The objectives were to describe parent-rated and physician-rated prognosis in a wide range of pediatric cancers and to describe the prevalence and predictors of parental prognostic optimism in poor prognosis pediatric cancer patients. METHODS: This Canadian multi-institutional cross-sectional study included children with cancer receiving any type of active treatment. The primary caregiver rated child prognosis on a 5-point categorical rating scale. For each child, five pediatric oncologists rated prognosis according to child- and disease-related characteristics. RESULTS: Of the 395 included families, 42 (10.6%) of parents rated prognosis as excellent or very good for children in whom physicians rated prognosis as poor. In multiple regression analysis, in comparison to parents of children with leukemia and lymphoma, parents of children with solid tumors (odds ratio (OR) 11.3, 95% CI 4.6, 27.8; P=0.0009) and brain tumors (OR 7.5, 95% CI 2.7, 21.1; P=0.09), parents of children with relapsed disease (OR 10.7, 95% CI 3.6, 31.3; P<0.0001) and parents with greater dispositional optimism (OR 1.1, 95% CI 1.0, 1.2; P=0.008) were more likely to have optimistic prognostic estimates in the setting of physician-rated poor prognosis. CONCLUSION: Approximately 10% of parents have optimistic prognostic estimates in the setting of physician-rated poor prognosis. Families of children with solid tumors and relapsed cancer and parents who were more optimistic were more likely to be optimistic in the poor prognosis setting. More research is needed to understand the impact of such discrepancies in prognosis on processes and outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".