Age at menopause and hormone replacement therapy as risk factors for head and neck and oesophageal cancer
Bibliographic record
Abstract
There were ~986,000 cases of head and neck cancer (HNC) and oesophageal cancer diagnosed worldwide in 2012. The incidence of these types of cancer is much higher in males than females, although this disparity decreases in the elderly population, suggesting a role for hormones as a risk factor. This systematic review investigates the potential role of female hormones [age at menopause and use of hormone replacement therapy (HRT)] as risk factors for HNC/oesophageal squamous cell carcinoma (SCC). The electronic databases MEDLINE, Web of Science, EMBASE and Cochrane were searched. Only studies with at least 50 cases of HNC/oesophageal SCC, with data on age at menopause, smoking, alcohol, age and socioeconomic status or educational attainment, were included. The Newcastle Ottawa Scale was used for assessing risk of bias. Eight studies met the inclusion criteria (5 oesophageal SCC, 2 HNC and 1 combined oesophageal SCC and HNC). HRT was shown to reduce the risk of HNC (HR, 0.78; 95% CI, 0.61-0.99) in one study. Our results showed that earlier age at menopause is a risk factor for oesophageal SCC, with women entering menopause at <45 years having double the risk of those entering menopause at age >50 years. Similar, but less striking, results were observed for HNC. HRT was found to reduce the risk of HNC/oesophageal SCC, but the evidence is inconclusive. We, therefore, recommend that consideration should be given to collecting data on reproductive factors and exposure to HRT, as routine practice, in future epidemiological and clinical studies of these cancers. The concept of oestrogen deficiency as a risk for HNC/oesophageal SCC deserves further exploration in future laboratory and clinical studies.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.006 |
| 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) | 0.003 | 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".