ITACA: A new validated international erythropoietic stimulating agent‐response score that further refines the predictive power of previous scoring systems
Bibliographic record
Abstract
BACKGROUND: In 'real-life', the Nordic score guides Erythropoietic stimulating agent (ESA) use in lower-risk myelodysplastic syndrome (MDS) with predicted response rates of 25% or 74%. As new treatments emerge, a more discriminating score is needed. OBJECTIVES: To validate existing ESA predictive scores and develop a new score that identifies non-responders. METHODS: ESA-treated patients were identified in 3 MDS registries in Italy and Canada (FISM 555, GROM 233, and MDS-CAN 208). Clinical and disease-related variables were captured. Nordic, MDS-CAN, and IPSS-R-based ESA scores were calculated and documented ESA responses compared. RESULTS: 996 ESA-treated patients were identified. Overall response rate (ORR) was 59%. The database was randomly divided into balanced derivation (n = 463) and validation (n = 462) cohorts. By multivariate analysis, transfusion independence, erythropoietin (EPO) level <100 IU/L, and IPSS low-risk were independently predictive of response. Assigning a score of 1 to each resulted in a scoring system of 0-3 with response rates of 23%, 43%, 67%, and 85%. ORR was concordant in the validation cohort. The 'ITACA' score had the highest discriminating power of response. CONCLUSION: ITACA is an internally-validated predictive SS of ESA response in real-life 'good risk' MDS patients derived from a large international dataset that surpasses others. The incorporation of biologic markers to better identify non-responders is still needed.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".