Bibliographic record
Abstract
OBJECTIVE: To provide a practical guide to help family physicians recognize, diagnose, and manage patients with pelvic venous disorders (PeVDs), often overlooked as causes of chronic pelvic pain and varicose veins. SOURCES OF INFORMATION: This review is based on guidelines from the American Venous Forum, the Society for Vascular Surgery, the American Vein and Lymphatic Society, the Society of Interventional Radiology, and the European Society for Vascular Surgery. MAIN MESSAGE: PeVDs are common, though frequently misdiagnosed, causes of chronic pelvic pain and varicose veins predominantly in female patients. These conditions arise from venous reflux or obstruction, which can cause varicose veins and venous hypertension in the renal hilum, pelvis, perineum, and lower extremities. Family physicians should recognize the clinical signs of PeVDs and use appropriate imaging to confirm diagnoses. Interventional treatments, including embolization and stenting, are effective for symptom management and improving patient outcomes. CONCLUSION: Early recognition of patients with PeVDs by family physicians is crucial for timely and effective treatment. By using appropriate diagnostic tools and making timely referrals, physicians can substantially improve patients' quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".