Evolving trend of systematic reviews and meta-analyses in endodontics: A bibliometric study
Bibliographic record
Abstract
Dear Editor, In their study of bibliometric analysis of systematic reviews and meta-analyses in endodontics, Lin et al.[1] found that the article with the most citations was “Outcomes of nonsurgical retreatment and endodontic surgery: A systematic review,”[2] with 184 citations (Scopus). This is followed by the second most-cited article entitled “Outcome of secondary root canal treatment: A systematic review of the literature”[3] with 182 citations (Scopus). However, the author observes that the most cited article is “Outcome of primary root canal treatment: Systematic review of the literature – Part 2. Influence of clinical factors”[4] with 480 citations (Scopus). This is followed by “Outcome of primary root canal treatment: Systematic review of the literature – Part 1. Effects of study characteristics on probability of success”[5] with 332 citations (Scopus). Since Lin et al.[1] compiled and examined the list of the top 100 most-cited articles, the flawed methodology to carry out article searching has raised serious questions about the credibility of this article’s findings. Missing the two most-cited articles[4,5] from the list of the 100 most-cited articles reflect a fundamental error in the article’s finding. Moreover, apart from the top-two most-cited articles, there are several articles which would be present in the Table 1 if correct methodology was applied. The author note that each bibliometric parameter assessed in the article is based on the top-cited articles; hence, the entire article is flawed and provides inaccurate information related to the topic.Table 1: The most-cited articles based on the citation counts
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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 | 0.263 | 0.646 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.152 | 0.181 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".