Systematic reviews on the success of dental implants present low spin of information but may be better reported and interpreted: An overview of systematic reviews with meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of spin and completeness of reporting of systematic reviews with metanalysis (SRMAs) in implant dentistry. STUDY DESIGN AND SETTING: Inclusion criteria were SRMAs of randomized clinical trials of implant dentistry on survival, success, or failure rates in humans, with no language restriction. Three databases were searched from inception to May 2021. Main outcomes were prevalence of spin (primary outcome) and completeness of reporting (secondary outcome) in abstracts and full texts. RESULTS: We identified 2481 SRMAs and 45 unique manuscripts were included. There was a low presence of spin in the abstracts and full text, except for adverse events, in which 51.1% (in the abstract) failed to mention any adverse event for summarized interventions. There was an adequate report of SRMAs in the full text except for prospective register (33.3% not reported). However, there was an incomplete report for most items in the abstract considering PRISMA-A checklist. CONCLUSION: In general, the included SRMAs presented a (a) low prevalence of spin (except for adverse events in the abstract); (b) adequate completeness of reporting in the full text (except for prospective register); and (c) incomplete report for most items in the abstracts.
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 | 0.164 | 0.428 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.025 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".