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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".