Societal preferences in the treatment of pediatric medulloblastoma: Balancing risk of death and quality of life
Bibliographic record
Abstract
PURPOSE: Medulloblastoma is the most prevalent childhood brain cancer. Children with medulloblastoma typically receive a combination of surgery, radiation, and chemotherapy. The survival rate is high but survivors often have sequelae from radiotherapy of the entire developing brain and spinal cord. Ongoing genetic studies have suggested that decreasing the dose of radiation might be possible among children with favorable molecular variants; however, this may result in an increased disease recurrence. As such, there is a need to investigate the nature of trade-offs that individuals are willing to make regarding the treatment of medulloblastoma. METHOD: We used best-worst scaling to estimate the importance of attributes affecting the general public's decision making around the treatment of medulloblastoma. After conducting focus groups, we selected three relevant attributes: (1) the accuracy of the genetic test; (2) the probability of serious adverse effects of the treatment(s); and (3) the survival rate. Using the paired method, we applied a conditional logit model to estimate preferences. RESULTS: In total, 3,006 respondents (51.3% female) with an average age of 43 years answered the questionnaires. All coefficients were statistically significantly different from zero and the attribute levels of adverse effects and the survival rate had the most impact on individuals' stated decision making. CONCLUSION: Overall, respondents showed high sensitivity to children experiencing disability particularly in the setting of a good prognosis. However, among children with poor prognostic molecular variants, participants showed tolerance about having a child with mild and partial disability compared to a low rate of survival.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".