Bibliographic record
Abstract
Introduction:The optimal duration of urethral catheterization after urethroplasty is unknown and published recommendations are not inclusive.This study aimed to synthesize existing evidence to evaluate the outcomes of different urinary catheter removal timing (early vs. late) after urethroplasty.Methods: We performed a comprehensive search of PubMed, Embase, the Cochrane Library, and the Web of Science from inception to August 7, 2022.Articles were initially screened by title and abstract, and subsequently by a full paper review before being included in the final analysis.All comparative studies that assessed the association between urethral catheterization duration and frequency of extravasation and recurrence rate in patients who underwent urethroplasty were included in the analysis.Exclusion criteria were case reports, case series, letters to editors, and non-English studies.The risk of bias was assessed using the Newcastle-Ottawa Scale (Figure 1).Results: Of the 439 relevant records in the literature databases, five studies involving 634 patients were included (Table 1).In all five studies, the extravasation rate was not significantly different between the early and late catheter removal groups.Among the three studies that reported recurrence rates, the recurrence rate was
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.291 | 0.049 |
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".