Association of frailty with clinical outcomes in myelofibrosis: a retrospective cohort study
Bibliographic record
Abstract
There is limited understanding of the impact of frailty on clinical outcomes in patients with myelofibrosis (MF). In this retrospective cohort study on 439 chronic phase MF patients [mean age: 68·7 ± 12 years; median follow-up: 3·4 years (IQR 0·4-8·6)] from 2004 till 2018, we used a 35-variable frailty index (FI) to categorise patient's frailty status as fit (FI < 0·2, reference), prefrail (FI 0·2-0·29) or frail (FI ≥ 0·3). The association of frailty with overall survival (OS) and cumulative JAK inhibitor (JAKi) therapy failure was measured using hazard ratio (HR, 95% CI). In multivariable analysis, prefrail (HR 1·7, 1·1-2·5) and frail patients (HR 2·9, 1·6-5·5), those with higher DIPSS score (HR 2·5, 1·6-3·9) and transfusion dependency (HR 1·9, 1·3-2·9) had shorter OS. In a subset analysis of patients on JAKi treatment (n = 222), frail patients (HR 2·5, 1·1-5·7), patients with higher DIPSS score (HR 1·7, 1·0-3·1) and transfusion dependence (HR 1·7, 1·1-2·7) had higher cumulative incidence of JAKi failure. Age, comorbidities, ECOG performance status, and MPN driver mutations did not impact outcomes. Thus, higher frailty scores are associated with worse OS and increased JAKi failure in MF, and is a superior indicator of fitness in comparison to age, comorbidities, and performance status.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".