Other‐cause mortality in incidental prostate cancer
Bibliographic record
Abstract
BACKGROUND: In incidental prostate cancer (IPCa), elevated other-cause mortality (OCM) may obviate the need for active treatment. We tested OCM rates in IPCa according to treatment type and cancer grade and we hypothesized that OCM is significantly higher in not-actively-treated patients. METHODS: Within the Surveillance, Epidemiology, and End Results database (2004-2015), IPCa patients were identified. Smoothed cumulative incidence plots as well as multivariable competing risks regression models were fitted to address OCM after adjustment for cancer-specific mortality (CSM). RESULTS: Of 5121 IPCa patients, 3655 (71%) were not-actively-treated while 1466 (29%) were actively-treated. Incidental PCa not-actively-treated patients were older and exhibited higher proportion of Gleason sum (GS) 6 and clinical T1a stage. In smoothed cumulative incidence plots, 5-year OCM was 20% for not-actively-treated versus 8% for actively-treated patients. Conversely, 5-year CSM was 5% for not-actively-treated versus 4% for actively-treated patients. No active treatment was associated with 1.4-fold higher OCM, even after adjustment for age, cancer characteristics, and CSM. According to GS, OCM reached 16%, 27%, and 35% in GS 6, 7, and 8-10 not-actively-treated IPCa patients, respectively and exceeded CSM recorded for the same three groups (2%, 6%, and 28%, respectively). CONCLUSION: Our results quantified OCM rates, confirming that in not-actively-treated IPCa patients OCM is indeed significantly higher than in their actively-treated counterparts (HR: 1.4). These observations validate the use of no active treatment in IPCa patients, in whom OCM greatly surpasses CSM (20% vs. 5%).
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".