How can we predict lymphorrhoea and clinically significant lymphocoeles after radical prostatectomy and pelvic lymphadenectomy? Clinical implications
Bibliographic record
Abstract
OBJECTIVE: • To identify clinical and pathological variables that may help clinicians in predicting, preventing and managing lymphorrhoea and clinically significant lymphocoeles (CSL), which are reported complications after pelvic lymphadenectomy (PLND) and retropubic radical prostatectomy (RRP). PATIENTS AND METHODS: • We prospectively analysed 552 consecutive men with prostate cancer who underwent RRP and PLND (2006-2008). • All patients had detailed clinical and pathological data prospectively recorded in an electronic database. Drains were removed when the amount of lymph was < 20 mL in the previous 24 h. A CSL was defined as the presence of a symptomatic lymphocoele requiring treatment. Lymphorrhoea was defined as the total amount of lymph drained by the drains until their removal. • Univariable and multivariable logistic regression models were used to test the association between all the predictors (age, body mass index, American Society of Anesthesiologists score, prostate volume, clinical stage, number of LNs removed, surgeon, pathological T and N stage) and the presence of CSL. • Univariable and multivariable linear regression models were also used to test the association between the available predictors and lymphorrhoea. RESULTS: • The median (range) number of LNs removed was 20 (1-63). Both linear and logistic multivariable regression analysis showed that the number of removed LNs and age were the only two statistically significant predictors of total amount of lymphorrhoea and CSL after RRP and PLND (both P < 0.01). • Specifically, the risk of developing a CSL increased by 5% for every LN removed. Similarly, every year of age increased the risk of having CSL by 5%. • The most informative thresholds for predicting CSL were 65 years of age and 20 LNs removed. • External iliac lymphadenectomy resulted in a higher associated risk of lymphorrhoea and CLS relative to obturator LN removal (P= 0.001 vs P= 0.1, respectively). CONCLUSIONS: • There was a positive association between the number of LNs removed and age at RRP with the amount of lymphorrhoea and the risk of developing a CSL. • The most informative thresholds in predicting CSL were 65 years of age and 20 LNs removed. External iliac lymphadenectomy resulted in a higher risk of lymphorrhoea and CLS relative to obturator LN removal.
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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 | 0.004 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".