Clinical aspects and polymorphisms in the <i>LTA</i>, <i>TNFA</i>, <i>LTB</i> genes and association with dental implant loss
Bibliographic record
Abstract
BACKGROUND: This study shows the relationship between host factors and environmental factors in the influence of susceptibility to loss of dental implants. PURPOSE: The aim of this study was to investigate the association of clinical aspects and tag SNPs of the genes LTA, TNFA, and LTB with dental implant loss. MATERIALS AND METHODS: The subjects consisted of 244 patients, divided into two groups: control group (C)-163 individuals who did not lose any implants, being in function for at least 6 months; and study group (S)-81 individuals who had lost at least one implant. DNA was collected from saliva, and the genotypes were determined by real time PCR. Univariate and multivariate analysis were employed p < .05. RESULTS: After multivariate analysis, dental implant loss remained associated with the presence of teeth (p = .011), a larger amount of placed implants (p = .001), and allelle C of rs2009658 of the LTA gene (p = .006). For the other tag SNPs of these studied genes, there was no association between the groups C and S with dental implants loss. CONCLUSION: Presence of teeth, number of placed implants and allele C of rs2009658 of LTA gene were associated with implant loss.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".