Comment on “Betel‐Quid Addictive Use Disorders and Oral Potentially Malignant Disorders and Oral Cancer in South, Southeast, and East Asia: A Systematic Review and Meta‐Analysis”
Bibliographic record
Abstract
We have read the interesting systematic review and meta-analysis conducted by Ko et al. (2024) titled “Betel-quid addictive use disorders and Oral potentially malignant disorders and Oral cancer in south, southeast, and East Asia: A systematic review and meta-analysis”, which evaluated the prevalence and association between betel-quid use behavior and oral malignancy. The study concluded that betel-quid use acts as a mediator, linking betel-quid use to a heightened risk of developing oral malignant disorders. However, we have some methodological concerns and suggestions for improving the analysis. Firstly, the authors performed a quality assessment using the JBI tool for prevalence studies. While this is appropriate for prevalence data, conducting a quality assessment using the Newcastle-Ottawa Scale (NOS) for the measure of association (Odds ratios) data would be beneficial. Additionally, performing a sensitivity analysis that excludes data from high-risk-of-bias studies would help elucidate the impact of potential bias on the pooled estimates. Furthermore, the inclusion of a Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) analysis could provide a structured approach to rating the quality of evidence in systematic reviews and meta-analyses (Dewidar et al. 2023). The authors used funnel plots to assess publication bias. However, employing Doi plots and the Luis Furuya-Kanamori (LFK) index could enhance the evaluation for meta-analyses with a small number of studies, as in this case (Furuya-Kanamori, Barendregt, and Doi 2018). These tools quantify asymmetry in a less subjective manner, potentially indicating the presence of publication bias. Navneet Jain: methodology, conceptualization. Muhammed Shabil: writing – original draft, methodology. Sanjit Sah: writing – review and editing. The authors have nothing to report. The authors declare no conflicts of interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".