<scp>STUDENT ABSTRACTS</scp> of <scp>ISPE</scp>'s 2024 Mid‐Year Meeting, Orlando, Florida, <scp>USA</scp>, 14–16 April 2024
Bibliographic record
Abstract
Background and Aims: Although antiepileptic drugs (AEDs) are widely used in the treatment of various neurologic disorders, observational studies have raised concern about an association between AEDs use and the risk of cancer.This study aimed to investigate this association using a meta-analysis of observational epidemiological studies.Methods: We searched PubMed and EMBASE from inception to April 2023 to identify relevant case-control and cohort studies.Two authors independently screened the reports, extracted the data, and evaluated the methodological quality of the included studies based on the Newcastle-Ottawa Scale (NOS).The primary outcome was the risk of cancer among AEDs users, expressed as a pooled odds ratio (OR) or relative risk (RR) and its 95% confidence interval (95% CI) based on a random-effects model.Subgroup analyses were conducted according to study design, type of cancer, and type of AEDs.Publication bias was evaluated using Begg's funnel plot and Egger's test.Sensitivity analyses were also conducted to explore the influence of each study on the pooled estimate by omitting a study one by one and re-analyzing.Results: A total of 15 case-control studies and 6 cohort studies were included in the final analysis.Overall, AEDs use was significantly associated iv with an increased risk of cancer (OR/ RR = 1.09; 95% CI 1.02-1.17) in a random-effects meta-analysis of all studies.In the subgroup meta-analysis of case-control studies, a significantly increased risk was observed (OR/RR = 1.24; 95% CI 1.06-1.45),whereas in the subgroup meta-analysis of cohort studies, a nonsignificant association was observed (OR/ RR = 0.98; 95% CI 0.92-1.04).Subgroup analyses by type of cancer in cohort studies showed a significant association between AEDs use and the increased risk of liver cancer (OR/RR = 1.45; 95% CI 1.29-1.64).In the subgroup meta-analysis of cohort studies, valproate showed a significantly protective effect on the risk of cancer, whereas other types of AEDs showed a nonsignificant association with the risk of cancer.Conclusion: The use of AEDs did not show a significant association with an increased risk of developing all cancers based on higher-level evidence obtained from cohort studies.However, this meta-analysis revealed a significant increase in the risk of liver cancer among AED users.Further prospective cohort studies are needed to better delineate this potential association.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.218 | 0.032 |
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".