Retrospective evaluation of a decision‐support algorithm (MIPOGG) for genetic referrals for children with neuroblastic tumors
Bibliographic record
Abstract
BACKGROUND: Neuroblastoma is the most common pediatric extracranial solid tumor. Germline pathogenic variants in ALK and PHOX2B, as well as other cancer predisposition genes, are increasingly implicated in the pathogenesis of neuroblastic tumors. A challenge for clinicians is the identification of children with neuroblastoma who require genetics evaluation for underlying cancer predisposition syndromes (CPS). PROCEDURE: We developed a decisional algorithm (MIPOGG) to identify which patients with neuroblastic tumors have an increased likelihood of an underlying CPS. This algorithm, comprising 11 Yes/No questions, evaluates features in the tumor, personal and family history that are suggestive of an underlying CPS. We assessed the algorithm's performance in a retrospective cohort. RESULTS: Two hundred and nine of 278 consecutive patients with neuroblastic tumors at The Hospital for Sick Children (2007-2016) had sufficient clinical data for retrospective application of the decisional algorithm. Fifty-one of 209 patients had been referred to genetics for CPS evaluation; 6/51 had a genetic or clinical confirmation of a CPS. The algorithm correctly identified all six children (Beckwith-Wiedemann (n = 2), Fanconi anemia, RB1, PHOX2B, chromosome duplication involving ALK) as requiring a genetic evaluation by using clinical features present at diagnosis. The level of agreement between the algorithm and physicians was 83.9%, with 15 more patients identified by the algorithm than by physicians as requiring a genetics referral. CONCLUSIONS: This decisional algorithm appropriately detected all patients who, following genetic evaluation, were confirmed to have a CPS and may improve the detection of CPS in patients with neuroblastic tumors compared with current practice.
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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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.002 | 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".