Cost-effectiveness of an Outpatient Uterine Assessment and Treatment Unit in Patients with Abnormal Uterine Bleeding
Bibliographic record
Abstract
Abnormal uterine bleeding (AUB) is one of the most common presenting complaints in our medical system with up to 30% of females affected by this condition. The current evaluation and management of AUB often requires multiple lengthy visits to both general practitioners and specialists. Advances in endoscopic technology have allowed clinicians to diagnose and treat women presenting with AUB in a single-visit within an outpatient uterine assessment and treatment unit (UATU). Unfortunately, the UATU is not the standard of care with very few locations in Canada providing this type of service. This thesis project aimed to synthesize data pertaining to efficacy and safety as well as to evaluate the cost-effectiveness of a UATU service model compared to usual care in diagnosing and treating AUB. To address the main aim for this thesis project, the first manuscript focuses on the hysteroscopic procedures that may be offered in a UATU. The manuscript includes a systematic review that synthesizes outcome measures surrounding efficacy, patient safety, and cost data of outpatient hysteroscopy compared to hysteroscopy performed in the operating room. The second manuscript is a cost-effectiveness modelling study that compares cost and effectiveness outcomes, including time to diagnosis and time to treatment of a UATU versus usual care for women who present with AUB. Data used to populate the cost-effectiveness model were obtained from a retrospective review of patient charts and the published literature. The systematic review found no statistically significant difference in the safety, efficacy, or patient tolerability between outpatient and intraoperative hysteroscopy procedures. This review helps provide further support for performing procedures outside of a traditional operating room without increasing patient harm or compromising efficacy. However, given the current available evidence and limited number of studies, findings should be interpreted with caution. The cost-effectiveness analysis found that a UATU is cost-effective when compared to usual care in diagnosing and treating patients who present with AUB. These two studies combined provide evidence to support that the UATU has the potential to improve gynecologic care by reducing wait-times to receiving diagnosis and treatment and to lower overall costs to the health care system.
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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 | 0.008 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".