Identifying low cancer‐specific mortality risk lymph node‐positive radical prostatectomy patients
Bibliographic record
Abstract
OBJECTIVES: To identify low cancer-specific mortality (CSM) risk lymph node-positive (pN1) radical prostatectomy (RP) patients. METHODS: Within Surveillance, Epidemiology and End Results database (2010-2015) pN1 RP patients were identified. Kaplan-Meier plots and multivariable Cox-regression (MCR) models were used. Pathological characteristics were used to identify patients at lowest CSM risk. RESULTS: Overall, 2197 pN1 RP patients were identified. Overall, 5-year cancer-specific survival (CSS) rate was 93.3%. In MCR models ISUP GG1-2 (hazard ratio [HR]: 0.12, p < 0.001), GG3 (HR: 0.14, p < 0.001), GG4 (HR: 0.35, p = 0.002), pT2 (HR: 0.27, p = 0.012), pT3a (HR: 0.28, p = 0.003), pT3b (HR: 0.39, p = 0.009), and 1-2 positive lymph nodes (HR: 0.64, p = 0.04) independently predicted lower CSM. Pathological characteristics subgroups with the most protective hazard ratios were used to identify low-risk (ISUP GG1-3 and pT2-3a and 1-2 positive lymph nodes) patients versus others (ISUP GG4-5 or pT3b-4 or ≥3 positive lymph nodes). In Kaplan-Meier analyses, 5-year CSS rates were 99.3% for low-risk (n = 480, 21.8%) versus 91.8% (p < 0.001) for others (n = 1717, 78.2%). CONCLUSIONS: Lymph node-positive RP patients exhibit variable CSS rates. Within this heterogeneous group, those at very low risk of CSM may be identified based on pathological characteristics, namely ISUP GG1-3, pT2-3a, and 1-2 positive lymph nodes. Such stratification scheme might be of value for individual patients counseling, as well as in design of clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".