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 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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".