Age and d<scp>PCR</scp> can predict relapse in <scp>CML</scp> patients who discontinued imatinib: The <scp>ISAV</scp> study
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Bibliographic record
Abstract
Imatinib is effective for the treatment of chronic myeloid leukemia (CML). However even undetectable BCR-ABL1 by Q-RT-PCR does not equate to eradication of the disease. Digital-PCR (dPCR), able to detect 1 BCR-ABL1 positive cell out of 10(7) , has been recently developed. The ISAV study is a multicentre trial aimed at validating dPCR to predict relapses after imatinib discontinuation in CML patients with undetectable Q-RT-PCR. CML patients under imatinib therapy since more than 2 years and with undetectable PCR for at least 18 months were eligible. Patients were monitored by standard Q-RT-PCR for 36 months. Patients losing molecular remission (two consecutive positive Q-RT-PCR with at least 1 BCR-ABL1/ABL1 value above 0.1%) resumed imatinib. The study enrolled 112 patients, with a median follow-up of 21.6 months. Fifty-two of the 108 evaluable patients (48.1%), relapsed; 73.1% relapsed in the first 9 months but 14 late relapses were observed between 10 and 22 months. Among the 56 not-relapsed patients, 40 (37.0% of total) regained Q-RT-PCR positivity but never lost MMR. dPCR results showed a significant negative predictive value ratio of 1.115 [95% CI: 1.013-1.227]. An inverse relationship between patients age and risk of relapse was evident: 95% of patients <45 years relapsed versus 42% in the class ≥45 to <65 years and 33% of patients ≥65 years [P(χ(2) ) < 0.0001]. Relapse rates ranged between 100% (<45 years, dPCR+) and 36% (>45 years, dPCR-). Imatinib can be safely discontinued in the setting of continued PCR negativity; age and dPCR results can predict relapse.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it