Is Adjuvant Therapy a Better Option for Esophageal Squamous Cell Carcinoma Patients Treated With Esophagectomy? A Prognosis Prediction Model Based on Multicenter Real-World Data
Bibliographic record
Abstract
OBJECTIVE: To construct a prediction model for more precise evaluation of prognosis which will allow personalized treatment recommendations for adjuvant therapy in patients following resection of ESCC. BACKGROUND: Marked heterogeneity of patient prognosis and limited evidence regarding survival benefit of various adjuvant therapy regimens pose challenges in the clinical treatment of ESCC. METHODS: Based on comprehensive clinical data obtained from 4129 consecutive patients with resected ESCC in a high-risk region in China, we identified predictors for overall survival through a 2-phase selection based on Cox proportional hazard regression and minimization of Akaike information criterion. The model was internally validated using bootstrapping and externally validated in 1815 patients from a non-high-risk region in China. RESULTS: The final model incorporates 9 variables: age, sex, primary site, T stage, N stage, number of lymph nodes harvested, tumor size, adjuvant treatment, and hemoglobin level. A significant interaction was also observed between N stage and adjuvant treatment. N1+ stage patients were likely to benefit from addition of adjuvant therapy as opposed to surgery alone, but adjuvant therapy did not improve overall survival for N0 stage patients. The C -index of the model was 0.729 in the training cohort, 0.723 after bootstrapping, and 0.695 in the external validation cohort. This model outperformed the seventh edition American Joint Committee on Cancer staging system in prognostic prediction and risk stratification. CONCLUSIONS: The prediction model constructed in this study may facilitate precise prediction of survival and inform decision-making about adjuvant therapy according to N stage.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".