Systematic reviews in restorative dentistry: discussing relevant aspects
Bibliographic record
Abstract
OBJECTIVE: This article aims to discuss key aspects of systematic reviews (SR) focusing on the improvement of the conduct and reporting. METHODS: Important aspects of SRs, such as prospective registration of the review protocol, basic structure, inclusion criteria, use of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement, confidence in the results and future directions are discussed. To determine relevant aspects, a search was conducted without date limitations in PubMed (October 15th, 2017) to identify SRs written in English evaluating clinical performance of direct composite resin restoration in permanent posterior teeth or comparing direct composite resin with other material/techniques. The quality of SRs included was assessed using the Assessing the Methodological Quality of Systematic Reviews 2 tool. RESULTS: Fifteen SRs were included. The overall confidence in the results of SRs was classified as critically low. Some aspects should be highlighted: SRs of in vitro studies are an important tool in restorative dentistry, and initiatives such as the PRISMA Statement and PROSPERO should be considered a standard code of practice. CONCLUSIONS: The compliance with and awareness of the discussed aspects may be a significant feature of the improvement of SR quality in the dentistry. CLINICAL SIGNIFICANCE: Initiatives such as the PRISMA Statement and PROSPERO should be taken in account by systematic reviewers in dentistry to improve the conduct and reporting of SRs, and to make their reviews are more clinically helpful.
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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 | 0.335 | 0.569 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.014 | 0.013 |
| 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".