Survey of Systematic Review Authors in Dentistry: Challenges in Methodology and Reporting
Bibliographic record
Abstract
The study reported in this article had three objectives: 1) identify the challenges faced by authors of dental systematic reviews (SR) during the process of literature search and selection; 2) determine whether dental SR authors' responses to survey questions about their study methodology were consistent with the reported published methodology; and 3) assess whether dental SR authors' evidence-based publication experience was associated with reported methodology. Seventy-eight authors (53 percent) of dental SRs out of 147 potential authors published from 2000 to 2006 responded to an online survey. According to the respondents, the most challenging aspects of literature search and selection were the initial design and performing extended literature searches. Agreement between the protocol identified by SR authors on the survey and the actual protocol described in their publications was fair to moderate. There were virtually no correlations between authors' publication experience, systematic review literature search, and selection thoroughness except for the number of past SRs published, and no differences in thoroughness between SRs written by clinicians (dental practitioners in the community) and dental school faculty members. Dental SR authors do not appear to fully appreciate the importance of extensive literature searches as central to the validity of their systematic review methods and potential findings.
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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 | 0.820 | 0.919 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.022 | 0.028 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".