The effect of hormone therapy on quality of life and breast cancer risk after risk-reducing salpingo-oophorectomy: a systematic review
Bibliographic record
Abstract
BACKGROUND: It is unclear if the use of hormone therapy (HT) in carriers of BRCA mutations improves the quality of life (QOL) without increasing the risk of breast cancer following a risk-reducing salpingo-oophorectomy (RRSO). Our objective was to assess the effect of HT on QOL and breast cancer risk, after RRSO. METHODS: We searched MEDLINE, EMBASE, CINHAL, and others, from inception to July 22, 2016, to identify relevant studies. Two reviewers independently screened identified records for controlled trials and observational studies that addressed the effect of HT on QOL and breast cancer risk in women with BRCA mutations, post RRSO. Two reviewers independently extracted data on populations, interventions, comparators, outcomes, and methodological quality. Studies addressing the same outcome were synthesized using written evidence summaries or tables. RESULTS: Of the 1,059 records identified, 13 met our inclusion criteria. All studies were observational. Six studies assessed the effect on QOL. Of these, 3 showed improvement in QOL with HT use. The risk of breast cancer was evaluated in 4 studies. The mean duration of follow-up was 2.6 years (range 0.1-19.1). The risk of breast cancer did not change with HT use in all 4 studies. CONCLUSIONS: Cumulative evidence from our review suggests that short-term HT use following RRSO improves QOL. The effect on breast cancer risk is still unclear. There are too few long-term studies to draw any strong conclusions. The need for future well-designed RCTs for more established evidence is imperative.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".