Low physical function following cancer diagnosis is associated with higher mortality risk in postmenopausal women
Bibliographic record
Abstract
BACKGROUND: Postmenopausal women with cancer experience an accelerated physical dysfunction beyond what is expected through aging alone due to cancer and its treatments. The aim of this study was to determine whether declines in physical function after cancer diagnosis are associated with all-cause mortality and cancer-specific mortality. METHODS: This prospective cohort study included 8068 postmenopausal women enrolled in the Women's Health Initiative with a cancer diagnosis and who had physical function assessed within 1 year of that diagnosis. Self-reported physical function was measured using the 10-item physical function subscale of the 36-Item Short Form Health Survey. Cause of death was determined by medical record review, with central adjudication and linkage to the National Death Index. Death was adjudicated through February 2022. RESULTS: Over a median follow-up of 7.7 years from cancer diagnosis, 3316 (41.1%) women died. Our results showed that for every 10% difference in the physical function score after cancer diagnosis versus pre-diagnosis, all-cause mortality and cancer-specific mortality were reduced by 12% (hazard ratio [HR] = 0.88, 95% confidence interval [95% CI] = 0.87 to 0.89 and HR = 0.88, 95% CI = 0.86 to 0.91, respectively). Further categorical analyses showed a significant dose-response relationship between postdiagnosis physical function categories and mortality outcomes (P < .001 for trend), where the median survival time for women in the lowest physical function quartile was 9.1 years (Interquartile range [IQR] = 8.6-10.6 years) compared with 18.4 years (IQR = 15.8-22.0 years) for women in the highest physical function quartile. CONCLUSION: Postmenopausal women with low physical function after cancer diagnosis may be at higher risk of mortality from all causes and cancer-related mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".