The Utility of the Beck Depression Inventory in the Evaluation of Chronic Pelvic Pain Patients
Bibliographic record
Abstract
Objective: The aim of this study to estimate if patients with chronic pelvic pain (CPP) and evidence of clinical depression suffer from a decreased prevalence of significant organic pelvic pathology when compared to euthymic CPP patients and patients undergoing laparoscopic tubal ligation. Design: In this observational cohort study (Canadian Task Force Classification II-2), 28 CPP and 30 tubal ligation patients were preoperatively administered the Beck Depression Inventory (BDI) and then assessed with diagnostic laparoscopy. Groups were then compared with respect to pelvic pathology and BDI scores. Measurements and Main Results: Patients with CPP had a greater overall rate of depressive comorbidity, with 57% (16/28) of the patients suffering from depression, based on our preoperative depression screen. In contrast, control patients suffered from depressive comorbidity only 10% (3/30) of the time. CPP patients had evidence of significant pathology 50% of the time, while asymptomatic tubal ligation patients demonstrated significant pathology 40% of the time. Subgroup analysis of CPP patients demonstrated that patients with CPP are equally likely to have abnormal pathology regardless of their depressive symptom scores. Conclusions: Consistent with prior studies, patients with CPP suffer from a greater prevalence of depression. Additionally, our study found no difference in the incidence of significant pelvic pathology in CPP patients with depression than those without. Consequently, the delay of diagnostic laparoscopy may hinder the timely diagnosis and treatment of CPP patients with evidence of clinical depression.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".