Can proliferative hypertrophic scars of the median sternotomy incision predict the occurrence and characteristics of urethral stricture?
Bibliographic record
Abstract
OBJECTIVES: To investigate the correlation between the characteristics of urethral stricture and incision scars in patients with urethral stricture and median sternotomy incision. Methods: We identified 368 patients who had undergone internal urethrotomy between January 2014 and December 2017. A total of 49 male patients with a median sternotomy scar and diagnosed with urethral stricture were retrospectively evaluated. The median sternotomy incision scars were assessed using the Vancouver Scar Scale (VSS) and the patients were divided into 2 groups. Group I consisted of patients with a VSS score of less than 4 points, and those with ≥4 points constituted group II. The groups were compared in terms of age, smoking habit, body mass index, diabetes mellitus, hypertension, urethral stricture etiology, length and localization, and stricture relapse after intervention. RESULTS: The mean total VSS score was 2.0 points in group I and 7.46 points in group II. There was a significant correlation between the VSS total score and the urethral stricture length among the whole study population (correlation coefficient value=0.481; p less than 0.001). The urethral stricture was longer as the VSS score increased. Conclusion: A poorly healed median sternotomy incision scar can predict a poor wound healing in the urethra tissue. Further large scale, multi-center and prospective studies are needed to clarify this relationship.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".