Prediction of sustained remission after tyrosine kinase inhibitor discontinuation with <i>BCR</i>::<i>ABL1</i> digital <scp>PCR</scp> in chronic myeloid leukemia patients
Bibliographic record
Abstract
Abstract Precise and reliable predictive parameters to accurately identify chronic myeloid leukemia (CML) patients who can successfully discontinue their tyrosine kinase inhibitor (TKI) treatment are lacking. One promising parameter is depth of molecular response measured by BCR::ABL1 digital PCR (dPCR). The aim of this study was to validate a previously described prediction cutoff of 0.0023% IS and to assess the value of dPCR for treatment‐free remission (TFR) prediction in relation to other clinical parameters. A droplet‐based dPCR assay assessed BCR::ABL1 % IS prior to TKI discontinuation. The primary endpoint was molecular recurrence (MolR) by 36 months. A total of 186 patients from Canada, Germany, and the Netherlands were included. In patients with a first TKI discontinuation attempt ( n = 163), a BCR::ABL1 dPCR < and ≥0.0023% IS had a MolR probability of 33% and 70%, respectively. Patients treated less than 6 years with a BCR::ABL1 dPCR <0.0023% IS had a MolR probability of 31%. After correction for treatment duration, both high dPCR value and the use of imatinib (vs. second‐generation TKI) were significantly associated with a higher risk of MolR (HR of 3.66, 95%CI 2.06–6.51, p < .001; and 2.85, 95%CI 1.25–6.46, p = .013, respectively). BCR::ABL1 dPCR was not associated with TFR outcome after second TKI discontinuation, however, with the limitation of a small number of patients analyzed ( n = 23). In conclusion, BCR::ABL1 digital PCR based on the cutoff of 0.0023% IS is a valuable predictive tool to identify CML patients with a high probability of TFR success after first TKI discontinuation, including patients treated for less than 6 years.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".