The Effect of Prophylactic Hepatoprotective Therapy on Drug-Induced Liver Injury in Patients Undergoing Chemotherapy for Cervical Cancer: A Retrospective Analysis Based on Propensity Score Matching
Bibliographic record
Abstract
This retrospective study aimed to assess the effectiveness of prophylactic hepatoprotective therapy in decreasing the incidence of drug-induced liver injury (DILI) among patients with cervical cancer undergoing chemotherapy. The analysis was performed on patients with cervical cancer who received chemotherapy at a tertiary hospital between September 2019 and August 2020. Propensity score matching (PSM) was utilized to equilibrate baseline characteristics between the treatment group, which received prophylactic hepatoprotective drugs, and the control group, which did not receive prophylaxis. The incidence and severity of liver injury were evaluated using the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Out of the 609 patients initially screened, 299 were included following PSM, with 105 in the treatment group and 194 in the control group. There were no significant differences in the incidence of liver injury (21.90% vs. 18.04%, p = 0.420) or its severity (p = 0.348) observed between the groups. Furthermore, none of the subgroups exhibited a significant reduction in DILI risk with prophylaxis. However, the number of patients experiencing an increase in their grade of liver injury was significantly higher in the treatment group (18.10% vs. 13.40%, p = 0.002), with these patients also exhibiting increased levels of alkaline phosphatase (ALP) and direct bilirubin (DBIL) post-chemotherapy (p < 0.05). Hepatoprotective drugs are not associated with a reduced risk of DILI and may in fact increase 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".