P124: Liquid biopsy for early cancer detection in children and adults with hereditary cancer syndromes across Canada
Bibliographic record
Abstract
Conclusion: CENAS holds significant promise as a point-of-care diagnostic tool for the rapid, low-cost, and bedside detection of PML::RARA fusions in APL patients.With early mortality rates still as high as 15% and approximately 10% of patients either resistant to initial therapy or experiencing relapse, there is a pressing need for more efficient diagnostic and therapeutic strategies.CENAS's ability to identify novel genomic factors associated with atypical PML::RARA fusions offers critical insights that could significantly enhance prognosis and inform treatment decisions in APL.These findings suggest that CENAS not only serves as an effective diagnostic tool for detecting fusion status, but also has the potential to uncover new genetic markers that may influence disease progression, therapeutic resistance, and treatment response.This could pave the way for more personalized, targeted therapies, ultimately improving outcomes for APL patients.By enabling the identification of previously undetected genomic factors, CENAS has the potential to transform both the diagnosis and management of APL.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".