Preoperative evaluation of the ‘hostile pelvis’ in radical prostatectomy with computed tomographic pelvimetry
Bibliographic record
Abstract
OBJECTIVE: To determine whether preoperative pelvimetry based on computed tomography (CT) can be used to predict technical difficulties during open radical prostatectomy (RP). PATIENTS AND METHODS: An open RP database accrued prospectively between January 1997 and June 2005 was used to identify 450 patients with preoperative pelvic imaging. Of these, 165 had adequate imaging of the pelvis with CT to allow pelvimetry using software provided with the medical imaging records. Several pelvic measurements were recorded in conjunction with body mass index and transrectal ultrasonographic estimates of prostatic volume. Outcome measures used to reflect technical surgical difficulties included operative duration, blood transfusion requirements within 30 days of RP, the pathological positive surgical margin and prostatic capsular breech rate. Logistic and linear regression analyses were used to determine the relationship between variables before and after RP. RESULTS: The selected pelvimetric measurements failed to predict either operative duration or the peri-operative blood transfusion requirement. Prostatic volume was predictive of operative duration; for every increase of 20 mL in prostate volume the duration increased by 8.4 min. Although pelvimetric measures failed to predict positive surgical margins at pathology, the transverse diameter predicted the likelihood of a positive margin due to capsular breech. With every 8.6 mm (1 sd) decrease in transverse diameter, the odds of a capsular breech resulting in positive surgical margins increased 5.3 times (95% confidence interval 2.1-20.0, P = 0.002). CONCLUSIONS: Although the "hostile pelvis" influences the likelihood of prostatic capsular breech resulting in positive surgical margins, CT pelvimetric screening of patients before RP is unlikely to be cost-effective. Routine pelvic CT in the evaluation of patients before RP is not supported.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".