An eHealth decision‐support tool to prioritize referral practices for genetic evaluation of patients with Wilms tumor
Bibliographic record
Abstract
Over 10% of children with Wilms tumor (WT) have an underlying cancer predisposition syndrome (CPS). Cognizant of increasing demand for genetic evaluation and limited resources across health care settings, there is an urgent need to rationalize genetic referrals for this population. The McGill Interactive Pediatric OncoGenetic Guidelines study, a Canadian multi-institutional initiative, aims to develop an eHealth tool to assist physicians in identifying children at elevated risk of having a CPS. As part of this project, a decisional algorithm specific to WT consisting of five tumor-specific criteria (age <2 years, bilaterality/multifocality, stromal-predominant histology, nephrogenic rests, and overgrowth features) and universal criteria including features of family history suspicious for CPS and congenital anomalies, was developed. Application of the algorithm generates a binary recommendation-for or against genetic referral for CPS evaluation. To evaluate the algorithm's sensitivity for CPS identification, we retrospectively applied the tool in consecutive pediatric patients (n = 180) with WT, diagnosed and/or treated at The Hospital for Sick Children (1997-2016). Odds ratios were calculated to evaluate the strengths of associations between each criterion and specific CPS subtypes. Application of the algorithm identified 100% of children with WT and a confirmed CPS (n = 27). Age <2 years, bilaterality/multifocality, and congenital anomalies were strongly associated with pathogenic variants in WT1. Presence of >1 overgrowth feature was strongly associated with Beckwith-Wiedemann syndrome. Stromal-predominant histology did not contribute to CPS identification. We recommend the incorporation of the WT algorithm in the routine assessment of children with WT to facilitate prioritization of genetic referrals in a sustainable manner.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".