An audit of implanted penile prostheses in the UK
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Associate Editor Michael G. Wyllie Editorial Board Ian Eardley, UK Jean Fourcroy, USA Sidney Glina, Brazil Julia Heiman, USA Chris McMahon, Australia Bob Millar, UK Alvaro Morales, Canada Michael Perelman, USA Marcel Waldinger, Netherlands OBJECTIVE To assess whether the outcome of implanting penile prostheses is related to the number of prostheses implanted by surgeons, as several reports showed that the outcome of a urological procedure is directly related to the experience of the surgeon. METHODS We conducted a retrospective audit of 413 penile prostheses implanted over a 2‐year period in the UK by 76 surgeons. RESULTS About 80% of the surgeons implanted only one or two prostheses per year, usually of the malleable type, and usually on patients in the private sector. Only four surgeons implanted >20 prostheses per year. The overall revision rate for implantation in the UK, at 24%, is far higher than the worldwide average. CONCLUSIONS Guidelines are needed on the number of prostheses a surgeon should implant per year so that revision rates will decline to more acceptable levels and patients will be offered a genuine choice of product.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it