Identifying Risk Factors of Endometrial Cancer and Recognition of Abnormal Uterine Bleeding during Perimenopause: Scoping Review
Bibliographic record
Abstract
Endometrial cancer is the most common gynecological cancer in the developed world with both incidence and mortality increasing. Recognizing the first presenting symptoms of abnormal uterine bleeding and identifying risk factors associated with endometrial cancer can lead to earlier diagnoses and improved outcomes. Despite increasing incidence, lack of awareness and knowledge of endometrial cancer-associated risk factors and symptoms remains prevalent. A recent priority setting exercise identified public awareness, risk factors, prevention, screening, as well as referral criteria for abnormal bleeding as top priority for both women with cancer and their treating physicians. A substantial proportion of women diagnosed with EC are of South Asian (i.e., people identifying as of Indian, Pakistani, or Bangladeshi ethnicity) and Black ethnicity, as well as older women, yet they are often underrepresented in research. Synthesizing the current literature related to symptom and risk factor recognition for endometrial cancer while taking socio-ethnic and -demographic characteristics into consideration can help identify trends or characteristics that may influence recognition and knowledge. This will allow for better tailoring of future education and screening efforts, as well as lead to a deeper understanding of the role of poor awareness in increasing incidence and mortality rates.
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.
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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".