Influence of local, systemic and surgeon related factors in dental implants osseointegration
Bibliographic record
Abstract
Os fatores que possam promover e incrementar a osseointegração, ou prejudicar o processo biológico, aumentando o índice de falhas, têm sido cada vez mais investigados com o objetivo de ampliar indicações e as taxas de sobrevivência dos implantes dentais assim como controlar fatores adversos. O objetivo desta tese foi identificar, analisar e sintetizar as evidências científicas quanto a influência das estatinas, do envelhecimento e da experiência do cirurgião no processo osseointegração e na sobrevivência de implantes dentais. Este volume apresenta um compilado de três revisões sistemáticas orientadas pelas recomendações PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). O processo de revisão foi realizado por meio de uma busca sistemática em cinco bases de dados eletrônicas (PubMed, Scopus, Web of Science, Embase e Cochrane Library), além de busca manual nas referências bibliográficas dos estudos incluídos. A meta-análise, quando cabível, foi realizada com o auxílio do software Review Manager (RevMan, Versão 5.3). O grau de heterogeneidade entre os estudos foi verificado por meio do teste Q de Cochran e I2. O viés das publicações foi avaliado com o auxílio das escalas Newcasttle-Ottawa e Cochrane Collaboration\'s Tool. A síntese dos dados permitiu a publicação de três artigos científicos dispostos nesta tese.
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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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".