Systematic Review and Meta-Analysis of Randomized Controlled Trials Assessing the Effect of Different Dental Disease Treatments on Preterm Birth Outcomes
Bibliographic record
Abstract
eview question / Objective Does the treatment of dental disease during pregnancy reduce the risk of preterm birth?Rationale Evidence suggests that dental disease is associated with preterm birth.There is some evidence from randomized controlled trials suggesting that treatment of some dental conditions may reduce the risk of preterm birth.This study aims to systematically review and metaanalyze evidence on the impact of various dental disease treatments on preterm birth and other obstetric adverse events. Condition being studiedThe condition being studied is dental diseases in pregnancy.This includes but is not limited to dental caries, gingivitis, gum disease/infection/inflammation/ disorder, periodontitis, periodontal disease/ infection/inflammation/disorder, oral bacterial infection. METHODSSearch strategy Eligible studies will be identified by a predefined search strategy of the electronic databases.The search strategy will be conducted with assistance from a medical information specialist.The search will be conducted in accordance with Peer Reviewed Electronic Search Strategies (PRESS) guidelines (2015).The search strategy will include terms related to Dental diseases such as dental caries; gingivitis; gum d i s e a s e / i n f e c t i o n / i n fl a m m a t i o n / d i s o r d e r ; periodontitis periodontal disease/infection/ inflammation/disorder; pulpitis, pericoronitis, oral bacterial infection; pregnancy granuloma; pregnancy gingivitis, dental disease treatments
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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 | 0.019 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.026 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".