The presence of intraductal carcinoma of the prostate is closely associated with poor prognosis
Bibliographic record
Abstract
We aimed to confirm the predictive ability of the presence of intraductal carcinoma of the prostate (IDC-P) for prognosis and the associations between IDC-P and clinicopathological parameters. Studies were identified in PubMed, Cochrane Library, EMBASE, Web of Science, and SCOPUS up to December 1, 2019. Hazard ratios (HRs) for survival data and odds ratios for clinicopathological data with 95% confidence intervals (CIs) were extracted. Heterogeneity was evaluated by the I 2 value, and quality was assessed by the Newcastle–Ottawa Scale criteria. A total of 4179 patients from 13 studies were included. The results showed that IDC-P presence was significantly associated with poor progression-free survival (PFS; HR = 2.31; 95% CI: 1.96–2.73), cancer-specific survival (HR = 1.89; 95% CI: 1.28–2.77), and overall survival (HR = 2.14; 95% CI: 1.53–3.01). In the subgroup analysis, IDC-P presence was significantly associated with poor PFS in prostate cancer treated by radical prostatectomy (HR = 2.48; 95% CI: 2.05–3.00) and treated by radiotherapy (HR = 2.83; 95% CI: 1.65–4.85). Regarding clinicopathological characteristics, patients with IDC-P presence had significantly higher tumor clinical stages, Gleason scores, probabilities of lymph node invasion, positive surgical margins, and positive extraprostatic extension. Our meta-analysis indicates that the presence of IDC-P is closely associated with poor prognosis and adverse clinicopathological characteristics. Our data support the value and clinical utility of the routine detection of IDC-P by pathological examination. These conclusions need further validation, and prospective studies are needed to find better treatment modalities other than traditional first-line therapy for patients with IDC-P.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".