Patient expectations and understanding of diagnosis entering an interdisciplinary chronic pelvic pain program: A cross-sectional study
Bibliographic record
Abstract
Background: Chronic pelvic pain is a complex condition. Few studies have focused on patient expectations of diagnosis and treatment in female chronic pelvic pain populations. Not knowing this information can lead to disparity and frustration between provider care and patient expectations resulting in poor patient outcomes. The aim of this study was to explore patient expectations and understanding of diagnosis prior to engaging in an interdisciplinary pelvic pain program. Methods: This is a cross sectional study of women enrolled in a tertiary Chronic Pain Center pelvic pain program in May 2019. Data were extracted from intake questionnaires to classify demographic variables and analyzed using descriptive statistics. Expectations and diagnoses were clustered in themes. Student’s t-test was used to compare biomedical focus and biopsychosocial focus groups to the Pain Disability Index (PDI) and the EQ-5D results, and to compare self-reported diagnoses with treatment expectations between groups. Results: When asked about perceived diagnosis, 74% reported a gynecologic cause for their pain, 25.7% reported musculoskeletal causes, and 21.9% reported other health conditions. For treatment expectations: 46.6% believed they required rehabilitation, 30.8% responded “I don’t know”, and 21.2% reported perceived need for medication. There was no difference in PDI or EQ-5D scores between patients who identified perceived treatment options and those who reported “I don’t know”. Conclusions: Most patients identified a perceived cause for their pain, but there was uncertainty and ambiguity about treatment options. Understanding and addressing the perception and expectations of individuals is imperative to patient-centered care and can lead to improved outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".