Prognostic Impact of Neoadjuvant Chemotherapy in Gallbladder Cancer: a Population- Based and Propensity Score Matched SEER Analysis
Bibliographic record
Abstract
Abstract Background: The effect of neoadjuvant chemotherapy (NACT) in gallbladder cancer (GBC) patients remains controversial. The aim of this study was to assess the impact of NACT on overall survival (OS), cancer specific survival (CSS), and to explore possible protective predictors for prognosis. Methods: GBC patients’ data were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients in the NACT and non-NACT groups were propensity score matched (PSM) 1:3, the Kaplan-Meier method and log-rank test were performed to analyze the impact of NACT on OS and CSS. Univariable and multivariable Cox regression models were applied to identify the possible prognostic factors. Results: Of the 5,003 cases diagnosed as stage I-III GBC according to AJCC 8th TNM stage, 64 NACT and 192 non-NACT patients remained after PSM. In all GBC patients, the median OS of the NACT and non-NACT was 31 and 20 months (log-rank P＜0.001), and the median CSS of NACT and non-NACT was 31 and 24 months (log-rank P=0.002). While in advanced GBC patients, the median OS of the NACT and non-NACT groups were 27 and 16 months (log-rank P＜0.001), respectively, and the median CSS of the NACT and non-NACT groups were 27 and 19 months (log-rank P=0.006), respectively. Multivariable Cox regression analysis showed that NACT, lymph node dissection (LND) and surgery type were positive protective factors for OS and CSS in GBC patients. Conclusions: Patients receiving NACT had significantly better survival than those that did not. NACT may provide therapeutic benefits for GBC patients, especially for those at an advanced stage. NACT combined with radical surgery improved the survival time of GBC patients. Therefore, NACT combined with surgery may provide a better treatment option for advanced GBC patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".