Bibliographic record
Abstract
OBJECTIVE: To review advances in the pharmacological treatment of erectile dysfunction (ED) highlighting potential neuro-endocrine and molecular targets. METHODS: A comprehensive MEDLINE and PUBMED search was utilized to investigate current novel mechanistic approaches in treating erectile dysfunction. Search criteria included, but were not limited to, erectile dysfunction (ED), cGMP, nitric oxide, Rho-kinase, phosphodiesterase, gene therapy, apomorphine, melanocortin and corpora cavernosa. Articles relating to in vitro, animal and in vivo experimental therapy were evaluated and included in this review and future directions are described. RESULTS: Current first-line ED treatment often involves the use of phosphodiesterase inhibitors. However, many novel pharmacotherapeutic approaches under development including the use of melanocortins and Rho-kinase inhibitors as well as the introduction of gene therapy have demonstrated efficacy in animal as well as early human trials. CONCLUSIONS: The success of oral pharmacotherapy for ED has been accompanied by renewed interest into the ED pathophysiology and signal transduction pathways. This has led to the identification of new therapeutic targets, which are poised to change the dynamic of ED management by broadening treatment alternatives to include other oral and genetic therapies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".