Bibliographic record
Abstract
OBJECTIVE: To review current knowledge about the epidemiology, etiology, diagnosis, and treatment of interstitial cystitis, with special emphasis on management of this condition by family physicians. QUALITY OF EVIDENCE: Articles were identified through MEDLINE and review of abstracts presented at Urology and Interstitial Cystitis meetings during the last decade. Recent reviews were further searched for additional studies and trials. Data were summarized from large epidemiologic studies. Etiologic theories were extracted from current concepts and reviews of scientific studies. Diagnostic criteria described in this review are based on clinical interpretation of National Institutes of Health (NIH) research guidelines, interpretation of data from the NIH Interstitial Cystitis Cohort Study, and recent evidence on use of the potassium sensitivity test. Treatment suggestions are based on six randomized placebo-controlled clinical treatment trials and best available clinical data. MAIN MESSAGE: Interstitial cystitis affects about 0.01% to 0.5% of women. Its etiology is unknown, but might involve microbiologic, immunologic, mucosal, neurogenic, and other yet undefined agents. The diagnosis of interstitial cystitis is a diagnosis of exclusion. It is impossible to provide a purely evidence-based treatment strategy, but review of available evidence suggests that conservative supportive therapy (including diet modification); oral treatment with pentosan polysulfate, amitriptyline, or hydroxyzine; and intravesical treatments with heparinlike medications, dimethyl sulfoxide, or BCG vaccine could benefit some patients. CONCLUSION: Family physicians should have an understanding of interstitial cystitis and be able to make a diagnosis and formulate an evidence-based treatment strategy for their patients.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".