RESPONSE: Re: 30-Day Mortality and Major Complications after Radical Prostatectomy: Influence of Age and Comorbidity
Bibliographic record
Abstract
We thank Russo et al. for sharing the results of their analysis of 30-day mortality among more than 4000 men who underwent radical prostatectomy in Milan, Italy, during a 10-year period. Similar to our findings that were recently reported in the Journal, the authors found an increased risk of 30-day mortality with increasing age (odds ratio [OR] = 2.7). This estimate is similar to both our unadjusted (OR = 2.5, 95% confidence interval [CI] = 1.5 to 4.2) and adjusted (OR = 2.0, 95% CI = 1.2 to 3.4) risk estimates. We do not know whether the odds ratio reported for age by Russo et al. was unadjusted or adjusted. Both our analysis and the data reported by Russo et al. suggest that cardiovascular disease (including coronary artery disease and congestive heart failure in our model and congestive heart failure only in the analysis by Russo et al.) is associated with 30-day mortality. Russo et al. do not report if they considered coronary artery disease separately. The wide confidence intervals around some of the estimates associated with comorbidity reported by Russo et al. suggest that few patients who underwent surgery had the conditions in question. Although the possible associations between age, comorbidity, and income reported by Russo et al. are intriguing, the very small number of deaths that occurred ( n = 17) severely constrains any multivariable modelling approaches. Indeed, in Russo et al.'s analysis, the association between income and mortality was not statistically significant. In our own dataset, because of privacy and confidentiality issues, we did not have access to income measures, so we are unable to confirm or refute these associations. The preliminary findings of Russo et al. therefore need replication in larger datasets. If the income-mortality association is true, the next step would be to understand the mechanism(s) by which income might impact early surgical mortality.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.012 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".