Performance of the eHealth decision support tool, MIPOGG, for recognising children with Li-Fraumeni, DICER1, Constitutional mismatch repair deficiency and Gorlin syndromes
Bibliographic record
Abstract
BACKGROUND: Cancer predisposition syndromes (CPSs) are responsible for at least 10% of cancer diagnoses in children and adolescents, most of which are not clinically recognised prior to cancer diagnosis. A variety of clinical screening guidelines are used in healthcare settings to help clinicians detect patients who have a higher likelihood of having a CPS. The McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG) is an electronic health decision support tool that uses algorithms to help clinicians determine if a child/adolescent diagnosed with cancer should be referred to genetics for a CPS evaluation. METHODS: This study assessed MIPOGG's performance in identifying Li-Fraumeni, DICER1, Constitutional mismatch repair deficiency and Gorlin (nevoid basal cell carcinoma) syndromes in a retrospective series of 84 children diagnosed with cancer and one of these four CPSs in Canadian hospitals over an 18-year period. RESULTS: MIPOGG detected 82 of 83 (98.8%) evaluable patients with any one of these four genetic conditions and demonstrated an appropriate rationale for suggesting CPS evaluation. When compared with syndrome-specific clinical screening criteria, MIPOGG's ability to correctly identify children with any of the four CPSs was equivalent to, or outperformed, existing clinical criteria respective to each CPS. CONCLUSION: This study adds evidence that MIPOGG is an appropriate tool for CPS screening in clinical practice. MIPOGG's strength is that it starts with a specific cancer diagnosis and incorporates criteria relevant for associated CPSs, making MIPOGG a more universally accessible diagnostic adjunct that does not require in-depth knowledge of each CPS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".