Prognosis model for overall survival in locally advanced pancreatic cancer (LAPC): An ancillary study of the LAP 07 trial.
Bibliographic record
Abstract
4024 Background: The management of LAPC patients remains controversial. Better discrimination for Overall Survival (OS) is needed to improve therapeutic decisions. We aimed to address this issue on the largest phase III cohort of LAPC by establishing the first prognosis model for OS with the full spectrum of parameters currently available at diagnosis. Methods: We enrolled the 442 LAPC patients recruited in LAP07, an international multicenter randomized phase III trial (NCT00634725). Thirty-five baseline variables among demographic, cancer history, clinical, biological and radiological parameters were evaluated in univariate and multivariate analyses as prognostic factors for OS. The predictive value of the final model was evaluated with Harrell’s C-index. This analysis was repeated 1000 times with the use of bootstrap sample to derive 95%CI for the C. A prognostic score was then developed based on the identified prognostic factors in the final model. Results: Independent prognostic factors identified in multivariate analysis (n=370) for OS were: age (HR= 1.01; 95%CI 1.00 - 1.03; p=0.0418), pain (HR= 1.36 ; 95%CI 1.08 - 1.71; p=0.0094), albumin (HR= 0.96; 95%CI 0.94 - 0.98; p=0.0001) and tumor size (HR= 1.01; 95%CI 1.00 - 1.02; p=0.0033). Harrell’s C-statistic for the final model was 0.60 (95%CI 0.56 - 0.63). A prognostic score between 0 and 4 was then calculated for each patient, based on the previous model. Three risk groups for death could be identified: “lower risk” (score∈[0,1]; n=17; median OS = 18.8 months; HR=1), “intermediate risk” (score∈(1,2]; n=166 ;median OS = 13.4 months; HR=1.7), “higher risk” (score∈(2,4]; n=187 ; median OS = 11.8 months; HR=2.1), p = 0.0101 by the global log rank test. Conclusions: Our results highlighted four OS’s independent pronostic factors among a broad spectrum of parameters at time of diagnosis.We have identified three groups with clearcut different prognostic profiles. The determination of this simple prognostic score should allow risk stratification that may help guiding clinical management of patients with LAPC and to design for future clinical trials. An external validation with a cohort issued from ARCAD meta-analysis is pending.
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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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".