Quality of Clinical Practice Guidelines for the Diagnosis and Treatment of Erectile Dysfunction: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Clinical practice guidelines (CPGs) guide the diagnosis and treatment of erectile dysfunction using different methodologies. Nonetheless, the quality of published CPGs is unknown. AIM: To evaluate the quality of CPGs for diagnosis and treatment of patients with erectile dysfunction. METHODS: The Medline, Embase, and LILACS databases were searched using structured strategies. The evidence was complemented by searches on websites of scientific societies and guideline developers. The CPG quality was assessed using the Appraisal of Guidelines for Research & Evaluation (AGREE II) instrument. MAIN OUTCOME MEASURE: The outcome measure included the quality of CPGs in accordance with the AGREE II instrument score. RESULTS: 17 guidelines met the selection criteria. 15 had recommendations for diagnosis, 16, had recommendations for treatment, and 1 included a follow-up. Most of the guidelines were developed in Europe (35.3%) and North America (29.4%), 2 were prepared by specialized groups (11.7%), and 1 was funded by public resources. The most common development method was the panel of experts (9 guides, 52.9%). 5 guidelines were of high quality as per the methodological rigor, as follows: Cancer Care Ontario 2016 (76.5%), European Urology Association 2018 (65.6%), American Urological Association 2018 (62.5%), American College of Physicians (62.5%), and Japanese Society for Sexual Medicine (60.4%). There was a significant relationship (P = .043) between the methodological quality of the guidelines and the funding source. CLINICAL IMPLICATIONS: By knowing the quality of the clinical practice guidelines, users can make more objective decisions about their use, which has an impact on patient care. STRENGTH & LIMITATIONS: High-quality CPGs frequently used in health-care practice were identified. Solely CPGs in Spanish, English, and Portuguese were included, which generates selection bias in the results. CONCLUSIONS: The number of CPGs for erectile dysfunction developed using international standards that meet the AGREE II quality criteria is low. Scientific societies have a strong interest in developing guidelines on this topic, whereas the participation of governmental organizations is limited. Sandoval-Salinas C, Saffon JP, Corredor HA. Quality of Clinical Practice Guidelines for the Diagnosis and Treatment of Erectile Dysfunction: A Systematic Review. J Sex Med 2020;17:678-687.
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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 | 0.062 | 0.272 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".