Cancer-specific mortality in breast cancer patients with hypothyroidism: a UK population-based study
Bibliographic record
Abstract
PURPOSE: Epidemiological studies have indicated a higher prevalence of hypothyroidism in breast cancer patients, possibly related to shared risk factors and breast cancer treatments. However, few studies have evaluated how hypothyroidism impacts survival outcomes in breast cancer patients. We aimed to determine the association between hypothyroidism and breast cancer-specific and all-cause mortality. METHODS: We conducted a population-based study using the Scottish Cancer Registry to identify women diagnosed with breast cancer between 2010 and 2017. A matched comparison cohort of breast cancer-free women was also identified. Using hospital diagnoses and dispensed prescriptions for levothyroxine, we identified hypothyroidism diagnosed before and after breast cancer diagnosis and determined associations with breast cancer-specific and all-cause mortality. Cox proportional hazards regression was used to calculate hazard ratios (HR) and 95% confidence intervals (CI) adjusted for potential confounders. RESULTS: A total of 33,500 breast cancer patients were identified, of which 3,802 had hypothyroidism before breast cancer diagnosis and 565 patients went on to develop hypothyroidism after. Breast cancer patients had higher rates of hypothyroidism compared with cancer-free controls (HR 1.14, 95% CI 1.01-1.30). Among breast cancer patients, we found no association between hypothyroidism (diagnosed before or after) and cancer-specific mortality (before: HR 0.99, 95% CI 0.88-1.12, after: HR 0.97, 95% CI 0.63-1.49). Similar associations were seen for all-cause mortality. CONCLUSION: In a large contemporary breast cancer cohort, there was little evidence that hypothyroidism, either at diagnosis or diagnosed after breast cancer, was associated with cancer-specific or all-cause 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".