Outpatient uterine assessment and treatment unit in patients with abnormal uterine bleeding: an economic modelling study
Bibliographic record
Abstract
BACKGROUND: Most often in Canada, the evaluation and management of abnormal uterine bleeding occurs under general anesthesia in the operating room. We aimed to assess the potential cost-effectiveness of an outpatient uterine assessment and treatment unit (UATU) compared with the current standard of care when diagnosing and treating abnormal uterine bleeding in women. METHODS: We performed a cost-effectiveness analysis and developed a probabilistic decision tree model to simulate the total costs and outcomes of women receiving outpatient UATU or usual care over a 1-year time horizon (Apr. 1, 2014, to Mar. 31, 2017) at a tertiary care hospital in Ontario, Canada. Probabilities, resource use and time to diagnosis and treatment were obtained from a retrospective chart review of 200 randomly selected women who presented with abnormal uterine bleeding. Results were expressed as overall cost and time savings per patient. Costs are reported in 2018 Canadian dollars. RESULTS: Compared with usual care, care in the UATU was associated with a decrease in overall cost ($1332, 95% confidence interval [CI] -$1742 to -$1008) and a decrease in overall time to treatment (-75, 95% CI -89 to -63, d). The point at which the UATU would no longer be cost saving is if the additional cost to operate and maintain the UATU is greater than $1600 per patient. INTERPRETATION: From the perspective of Canada's health care system, an outpatient UATU is more cost effective than usual care and saves time. Future studies should focus on the relative efficacy of a UATU and the total budget required to operate and maintain a UATU.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".