Age at menopause and hormone replacement therapy as risk factors for head and neck and oesophageal cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it