Need for clarification of data in the recent meta‐analysis about p53 polymorphism and gastric cancer risk
Bibliographic record
Abstract
We read with great interest the recent meta-analysis by Zhou et al.,1 which has reached important conclusions about the association between p53 codon 72 polymorphism and gastric cancer risk. Nevertheless, close inspection of the data provided by the authors (Table 1) revealed an issue that is worth mentioning. Specifically, the data reported by Zhou et al.1 for the study by Hamajima et al.2 do not seem in line with the data provided by Hamajima et al.2 in their original publication. The numbers reported by Zhou et al. for Arg/Arg, Pro/Arg, Pro/Pro, in cases and controls, respectively, are 54-64-26 and 85-117-39.1 Interestingly enough, after carefully studying the data presented by Hamajima et al.,2 the frequencies that we have retrieved on the 144 cases and 239 controls (241 minus 2, which had not been genotyped) were 51-70-23 and 90-106-43, respectively. This may imply that the original odds ratio for the study by Hamajima et al.2 may significantly differ from that calculated by Zhou et al.1 As a result, it would be valuable if the authors could provide a new, more accurate estimation of the pooled odds ratio after taking into account this remark. We believe that this remark will contribute to further, more accurate elaboration and substantiation of the original results presented by Zhou et al.1 Konstantinos P. Economopoulos, Theodoros N. Sergentanis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.338 | 0.681 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.011 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.012 | 0.007 |
| Research integrity | 0.014 | 0.033 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".