Methodological quality and descriptive characteristics of prosthodontic‐related systematic reviews
Bibliographic record
Abstract
Ideally, healthcare systematic reviews (SRs) should be beneficial to practicing professionals in making evidence-based clinical decisions. However, the conclusions drawn from SRs are directly related to the quality of the SR and of the included studies. The aim was to investigate the methodological quality and key descriptive characteristics of SRs published in prosthodontics. Methodological quality was analysed using the Assessment of Multiple Reviews (AMSTAR) tool. Several electronic resources (MEDLINE, EMBASE, Web of Science and American Dental Association's Evidence-based Dentistry website) were searched. In total 106 SRs were located. Key descriptive characteristics and methodological quality features were gathered and assessed, and descriptive and inferential statistical testing performed. Most SRs in this sample originated from the European continent followed by North America. Two to five authors conducted most SRs; the majority was affiliated with academic institutions and had prior experience publishing SRs. The majority of SRs were published in specialty dentistry journals, with implant or implant-related topics, the primary topics of interest for most. According to AMSTAR, most quality aspects were adequately fulfilled by less than half of the reviews. Publication bias and grey literature searches were the most poorly adhered components. Overall, the methodological quality of the prosthodontic-related systematic was deemed limited. Future recommendations would include authors to have prior training in conducting SRs and for journals to include a universal checklist that should be adhered to address all key characteristics of an unbiased SR process.
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 | 0.402 | 0.742 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.034 | 0.040 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| 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".