Prevalence of frailty and its association with clinical outcomes in myeloproliferative neoplasms: a population-based study
Bibliographic record
Abstract
Clinical implications of frailty in myeloproliferative neoplasms (MPN), including essential thrombocythemia (ET), polycythemia vera (PV), and myelofibrosis (MF), are unknown. In this population-based study, all incident cases of MPN from the Ontario cancer registry between 2004 and 2019 (N = 10 336; ET = 5108; PV = 3843; MF = 1385) and their matched controls (for age, sex, residence, and income) in a 1:4 ratio were included. Baseline frailty measured using the Johns Hopkins Adjusted Clinical Groups frailty indicator and McIsaac frailty index (mFI), categorized as fit, prefrail, or frail if mFI <0.10, 0.11 to 0.20, >0.20), was significantly higher in ET, PV, and MF compared with matched controls (standardized mean difference of 0.27, 0.27, and 0.28). Over 23%, 20%, and 34% of patients with ET, PV, and MF were frail or prefrail despite a younger age (<65 years) or minimal comorbidities. In Cox proportional regression, frailty was independently associated with worse overall survival (OS) after adjusting for age, sex, and comorbidities compared with mFI-fit patients. The hazard ratios (95% confidence interval) for OS for mFI-prefrail and mFI-frail patients were: 1.6 (1.3-1.9) and 3.6 (2.9-4.4) in ET, 1.3 (1.1-1.5) and 2.7 (2.1-3.4) in PV, and 1.2 (1.0-1.5) and 2.0 (1.5-2.7) in MF. Patients with MPN have a substantially higher prevalence of frailty compared with matched controls, which is associated with reduced OS, independent of age or comorbidities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".