Comprehensive evaluation of time‐to‐response parameter as a predictor of treatment failure following imatinib therapy in chronic phase chronic myeloid leukemia: Which parameter at which time‐point does matter?
Bibliographic record
Abstract
Early recognition of high-risk patient is important to improve long-term outcomes following imatinib therapy for chronic myeloid leukemia (CML). Some controversy surrounds the question, which of short-term response parameters at which time-point, including complete cytogenetic response (CCyR) or major molecular response (MMR) at 6 or 12 months, is the best predictor for treatment outcomes. In this comprehensive analysis, we adopted landmark analysis method, time-dependent Cox's proportional hazard model, and receiver-operating characteristics (ROC) method to analyze time-to-response parameter as predictor of long-term outcomes in 187 chronic phase (CP) CML patients. Regardless of the methods of analysis, earlier achievement of short-term response such as CCyR or MMR could predict the higher probability of achieving better interim outcome (such as treatment failure or loss of response [LOR]). Similar to the findings from other studies, our ROC analysis provided cutoff time points for MMR (18-36 months) and CCyR (6-12 months) that were the best predictors for LOR or treatment failure, which can be an indirect evidence supporting the ELN recommendation. The patient who achieves short-term response rapidly will have a lower risk of losing response or failing after imatinib therapy in CML patients.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".