Further evidence that endometriosis is related to tubal and ovarian cancers: A study of 271,444 inpatient women
Bibliographic record
Abstract
OBJECTIVE: To evaluate associations between endometriosis and tubal and ovarian cancers in a large population-based study. METHODS: The Health Care Cost and Utilization Project - National Inpatient Sample databases from 2005 to 2014 were used in this study. Data on patients with a diagnosis of tubal or ovarian cancer and endometriosis (overall and subtypes including adenomyosis and pelvic endometriosis) using International Classification of Diseases, Ninth Edition, Clinical Modification codes were extracted. Logistic regression analysis was performed to evaluate associations between tubal and ovarian cancers and endometriosis. Adjustment was made for age, race, median income level, payment plan, hospital location and obesity. RESULTS: Of 38,800,139 women aged >18 years who were hospitalized between 2005 and 2014, 271,444 women with adenomyosis and/or pelvic endometriosis, 4289 women with tubal cancer and 133,253 women with ovarian cancer were identified. The rate of tubal cancer was three-fold higher in women with endometriosis compared with women without endometriosis (0.03 % vs 0.01 %). The odds ratio (OR) adjusted for age, race, obesity, income and insurance type was 4.02 [95 % confidence interval (CI) 3.17-5.11; p < 0.01]. The rate of tubal cancer was higher in women with adenomyosis (0.04 % vs 0.01 %; adjusted OR 4.88, 95 % CI 3.66-6.50; p < 0.01) and women with pelvic endometriosis (0.02 % vs 0.01 %; adjusted OR 2.80, 95 % CI 1.84-4.27; p < 0.01) compared with women without these conditions. Similar associations were found between ovarian cancer and pelvic endometriosis and ovarian cancer and adenomyosis. CONCLUSION: Both pelvic endometriosis and adenomyosis are strongly associated with tubal and ovarian cancers.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".