Health Care Utilization by Patients With Chronic Pelvic Pain
Bibliographic record
Abstract
OBJECTIVE: To describe the patterns of health care utilization among patients with chronic pelvic pain. METHODS: Deidentified administrative claims data from the OptumLabs Data Warehouse were used. Adult female patients who had their first medical claim for chronic pelvic pain between January 1, 2016, and December 31, 2019, were included. Utilization was examined for 12 months after the index diagnosis. The greedy nearest neighbor matching method was used to identify a control group of individuals without chronic pelvic pain. Comparisons were made between those with and those without chronic pelvic pain using χ 2 tests for categorical data and Wilcoxon rank-sum tests for continuous data. RESULTS: In total, 18,400 patients were analyzed in the chronic pelvic pain cohort. Patients with chronic pelvic pain had a higher rate of chronic overlapping pain conditions. Patients with chronic pelvic pain had higher rates of health care utilization across all queried indices. They had more outpatient office visits; 55.5% had 10 or more office visits. Patients with chronic pelvic pain showed higher utilization of the emergency department (ED) (6.3 visits vs 1.9 visits; P <.001). Urine culture and pelvic ultrasonography were the most utilized tests. One-third of patients with chronic pelvic pain utilized physical therapy (PT), and 13% utilized psychological or behavioral therapy. Patients with chronic pelvic pain had higher rates of hysterectomy (8.9% vs 0.6%). The average total health care costs per patient with chronic pelvic pain per year was $12,254. CONCLUSION: Patients with chronic pelvic pain have higher rates of chronic overlapping pain conditions and undergo more ED visits, imaging tests, and hysterectomies than patients without chronic pelvic pain. Improving access to multidisciplinary care, increasing utilization of interventions such as PT and psychological or behavioral therapy, and reducing ED utilization may be possible targets to help reduce overall health care costs and improve patient care.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".