Hospital Emergency Room Visits for Heavy Menstrual Bleeding
Bibliographic record
Abstract
Heavy menstrual bleeding (HMB) is a well-recognized health issue among women of reproductive age. The characteristics and demographics of women in the community with HMB who present at the emergency department (ED) are not well described. This research aimed to describe the cohort of women who seek treatment at the ED. The Healthcare Cost and Utilization Project 2016 National Emergency Department Sample was examined. The theory of reasoned action predicts that women who present at the ED are exhibiting health seeking behavior. From 32 million records, the inception cohort identified N = 111,555 cases. Using national estimation weights, this translates to approximately 509,833 ED visits in the United States for HMB. The majority of the cohort, 39.59%, came from the lowest median household income quartile by ZIP code (under $42,999). Women with anemia were significantly older than the overall cohort. The greatest incidence of anemia was in women aged 40 to 49 years, 7.41%. Four logistic regression models examining the whole cohort and three comorbidities (anemia, hypertension, and diabetes) found age to be a significant predictor of hospitalization. Low income was also a significant predictor of hospitalization. The proportion hospitalized in the lowest household income group was significantly greater versus each of the other three quartiles, p < 0.001. Women living in the rural locations with the lowest household income had the highest proportion of hospitalizations. Women of economic disadvantage are most likely to use the ED for medical care. Residing in rural areas may lead to health avoidance until the severity of symptoms necessitates hospitalization. These findings signify a silent public health burden.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".