Pathological factors associated with survival benefit from adjuvant chemotherapy ( <scp>ACT</scp> ): a population‐based study of bladder cancer
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether pathological factors are associated with differential effect of adjuvant chemotherapy (ACT). PATIENTS AND METHODS: In this population-based retrospective cohort study, we linked electronic records of treatment and surgical pathology to the Ontario Cancer Registry. The study population included all patients with muscle-invasive bladder cancer undergoing cystectomy in Ontario 1994-2008. Factors associated with overall (OS) and cancer-specific survival (CSS) were evaluated using Cox proportional hazards. We tested for interaction between the following variables and ACT effect-size: N-stage, margin status, T-stage, and lymphovascular invasion (LVI). RESULTS: The study population included 2802 patients; 19% were treated with ACT. Interaction terms with ACT for OS/CSS are: N-stage (both P < 0.001); margin status (P = 0.054/P = 0.048); T-stage (P = 0.509/P = 0.286); and LVI (P = 0.361/P = 0.405). Magnitude of effect for ACT was greater for patients with node-positive disease [OS: hazard ratio (HR) 0.56, 95% confidence interval (CI) 0.47-0.67; CSS: HR 0.60, 95% CI 0.49-0.72] than for patients with node-negative disease (OS: HR 0.80, 95% CI 0.61-1.03; CSS: HR 0.79, 95% CI 0.59-1.07). ACT was also associated with greater effect among patients with involved margins (OS: HR 0.45, 95% CI 0.33-0.62; CSS: HR 0.40, 95% CI 0.28-0.57) compared with patients with negative margins (OS: HR 0.75, 95% CI 0.65-0.87; CSS: HR 0.79, 95% CI 0.67-0.93). CONCLUSIONS: In this population-based cohort study we observe evidence of interaction between ACT effect and nodal stage and surgical margin status. Our results suggest that patients at highest risk of disease recurrence may derive greatest benefit from ACT.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".