A visual approach to providing prognostic information to parents of children with retinoblastoma
Bibliographic record
Abstract
OBJECTIVE: Parents must rapidly assimilate complex information when a child is diagnosed with cancer. Education correlates with the ability to process and use medical information. Graphic tools aid reasoning and communicate complex ideas with precision and efficiency. METHODS: We developed a graphic tool, DePICT (Disease-specific electronic Patient Illustrated Clinical Timeline), to visually display entire retinoblastoma treatment courses from real-time clinical data. We report retrospective evaluation of the effectiveness of DePICT to communicate risk and complexity of treatment to parents. We assembled DePICT graphics from multiple children on cards representing each stage of intraocular retinoblastoma. Forty-four parents completed a 14-item questionnaire to evaluate the understanding of retinoblastoma treatment and outcomes acquired from DePICT. RESULTS: As a proposed tool for informed consent, DePICT effectively communicated knowledge of complex medical treatment and risks, regardless of the education level. We identified multiple potential factors affecting parent comprehension of treatment complexity and risk. These include language proficiency (p=0.005) and age-related experience, as younger parents had higher education (p=0.021) but lower comprehension scores (p=0.011), regardless of first language. CONCLUSION: Provision of information at diagnosis concerning long-term treatment complexity helps parents of children with cancer. DePICT effectively transfers knowledge of treatments, risks, and prognosis in a manner that offsets parental educational disadvantages.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".