[Curative effect on aggressive B cell non-Hodgkin's lymphoma and relevant influencing factors of its prognosis, a study of 203 cases].
Bibliographic record
Abstract
OBJECTIVE: To analyze the curative effect of aggressive B cell non-Hodgkin's lymphoma and relevant influencing factors of its prognosis. METHODS: The curative effects of aggressive non-Hodgkin's lymphoma which were diagnosed as diffuse small cleaved (E), diffuse mixed (F), diffuse large cell (G) or diffuse immunoblastic lymphoma (H) pathologically and certified as B cell lymphoma immunohistologically and treated with CHOP or other ADM-containing regimens for at least 4 cycles from 1993 to 1997 were analyzed. An analysis of the correlation of prognosis with different factors was made. RESULTS: The curative effects of 191 out of the 230 cases were evaluated. 131 (68.9%) patients achieved CR, and 47 (24.6%) achieved PR. The 6-year overall survival (OS) rates and progress free survival (PFS) rates were 51.4% and 43.3% respectively. According to IPI risk stratification, the 6-year OS rates were 66.6%, 42.2%, 23.6%, and 10.0% in the low-risk, low-intermediate-risk, high-intermediate-risk, and high risk groups respectively (P = 0.000 0). The 6-year PFS rates were 59.7%, 30.2%, 16.7%, and 10.3% in the low-risk, low intermediate risk, high-intermediate-risk, and high risk groups respectively. The prognosis analysis showed that OS and PFS were significantly correlated with different clinical characteristics such as stage, PS, number of extra-nodal lesions, LDH bulky disease, and B symptom. CONCLUSION: The treatment with CHOP as its main component has a beneficial effect on aggressive B cell lymphoma in low risk group, but is less effective for the low-intermediate, high-intermediate, and high risk.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".