Age and d<scp>PCR</scp> can predict relapse in <scp>CML</scp> patients who discontinued imatinib: The <scp>ISAV</scp> study
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".