[Clinical analysis of liver damage of 116 malignant lymphoma patients with chronic HBV infection after cytotoxic chemotherapy].
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Chronic hepatitis B virus (HBV) infection increases the prevalence of liver damage and related death of malignant tumor patients. This study was to investigate the prevalence of liver damage and clinical results in lymphoma patients with chronic HBV infection after standard chemotherapy, and assess high risk factors associated with liver damage for better guidance in clinic. METHODS: Records of 116 lymphoma patients with chronic HBV infection, treated with standard chemotherapy from Jan. 1985 to Jan. 2002 in Cancer Center of Sun Yat-sen University, were reviewed to analyze the prevalence of liver damage, clinical results, and related high risk factors. RESULTS: Of the 116 patients, 60 (51.7%) suffered liver damage. According to WHO criteria of liver toxicity, 4 (3.4%) were in grade I, 14 (12.1%) in grade II, 15 (12.9%) in grade III, and 27 (23.3%) in grade IV. After treatment for liver damage, 11 (9.5%) patients completed chemotherapy without delay, 27(23.3%) completed chemotherapy with delay of more than 8 days, 16 (13.8%) terminated chemotherapy, 6 (5.2%) died. Logistic multivariate analysis showed that steroid was a high risk factor of liver damage after chemotherapy. CONCLUSIONS: The prevalence of liver damage is high in lymphoma patients with chronic HBV infection after standard chemotherapy, which led to treatment delay or discontinue, even death. Steroid is a high risk of liver damage.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".