Bibliographic record
Abstract
Treating patients with postoperative stress urinary incontinenceTo the Editor: Allow me to preface my remarks with a speculation that I am the only member of the CUA whose practice has been severely hampered due to the destruction from Hurricane Ike.(I joined the faculty of the University of Texas Medical Branch in Galveston, Tex, just before the storm.)In any case, I enjoyed the articles concerning the treatment of male stress urinary incontinence postprostatectomy published in a recent issue of CUAJ.1,2 I think we can learn much from the experience with female urology and slings.Before the development of relatively loose suburethral Prolene slings, sling surgery was fraught with problems, including the adjustment of sling tension.The Burch, Marshall-Marchetti and bladder neck suspensions have problems as well, with the Burch suspension being accepted as the gold standard.The main problem with the new slings was mainly adjusting the tension, but the results have basically been good.In addition, it is possible to visualize the sling postoperatively for any problems, for example, with looseness.Translating this to male patients, slings are primarily for poor support, although slings can work for intrinsic sphincter deficiency.However, we don't currently have the tools to identify poor support.We need assistance from our imaging colleagues in the same way we can now image female pelvic floor prolapse and dysfunction.The problem isn't large volume loss versus less volume loss, so much as understanding the mechanism of incontinence.Theoretically, poor support should respond to a relatively loose sling (without bone
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.006 | 0.009 |
| 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".