Aspirin use and survival after the diagnosis of breast cancer: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Aspirin use has been associated with a reduced cancer incidence and fewer deaths from cancer. This study examined whether women with breast cancer prescribed aspirin postdiagnosis had improved survival. METHODS: An observational, population cohort study was undertaken using data linkage of cancer registry, dispensed prescriptions and death records in Tayside, Scotland. All community prescriptions for aspirin in women with breast cancer were extracted and use postdiagnosis for each individual examined using Cox's proportional hazard models. The main outcome measures were all-cause mortality and breast cancer-specific mortality. RESULTS: Four thousand six hundred and twenty-seven patients diagnosed with breast cancer between 1 January 1998 and 31 December 2008 were followed up until 28 February 2010. Median age at diagnosis was 62 (IQR 52-74). One thousand eight hundred and two (39%) deaths were recorded, with 815 (18%) attributed to breast cancer. One thousand and thirty-five (22%) patients were prescribed aspirin postdiagnosis. Such aspirin use was associated with lower risk of all-cause mortality (HR=0.53, 95% CI=0.45-0.63, P<0.001) and breast cancer-specific mortality (HR=0.42, 95% CI=0.31-0.55, P<0.001) after adjusting for age, socioeconomic status, TNM stage, tumour grade, oestrogen receptor status, surgery, radiotherapy, chemotherapy, adjuvant endocrine therapy and aspirin use prediagnosis. CONCLUSIONS: Aspirin use postdiagnosis of breast cancer may reduce both all-cause and breast cancer-specific mortality. Further investigation seeking a causal relationship and which subgroups of patients benefit most await ongoing randomised controlled trials.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".