Evaluation of Ki-67 as a Prognostic Marker in Diffuse Large B-Cell Lymphoma—A Single-Center Retrospective Cohort Study
Bibliographic record
Abstract
Background: Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma and prognostic information is essential in finding the right treatment. This study evaluated the prognostic significance of Ki-67 in patients with DLBCL. Methods: Patients with DLBCL, treated with first-line R-CHOP, were retrospectively analyzed in groups of high (>70%) and low (≤70%) Ki-67. Parameters of interest were the international prognostic index (IPI), treatment response, progression-free survival (PFS) and overall survival (OS). A chi-squared test or Fisher’s exact test was conducted to analyze categorical variables. Kaplan–Meier and log-rank tests were applied for survival analyses. Finally, a multivariate linear regression analysis was performed, including gender, Ki-67 ≤ 70% or >70%, IPI and presence of B symptoms. Results: Overall, 58 patients were included. No significant association was found between Ki-67 status and IPI (p = 0.148) or treatment response (p = 0.373). Survival in patients with high Ki-67 was significantly inferior with respect to OS (p = 0.047) but not PFS (p = 0.138). Multivariate linear regression, however, yielded only IPI as a risk factor for OS. Conclusion: Future studies with larger patient cohorts are needed in order to elucidate the prognostic role of Ki-67 in patients with DLBCL treated with R-CHOP.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".