<scp>L</scp>actotransferrin Gene (<scp><i>LTF</i></scp>) Polymorphisms and Dental Implant Loss: A Case‐Control Association Study
Bibliographic record
Abstract
BACKGROUND: Dental implants have been widely used to replace missing teeth, accomplishing aesthetics and function. Due to its large use worldwide, the small percentage of implant loss becomes significant in number of cases. Lactotransferrin (LTF) is a pleiotropic protein, expressed in various body tissues and fluids, which modulates the host immune-inflammatory response and bone metabolism, and might be involved in dental implant osseointegration. Recently, a few studies have been investigating genetic aspects underlying dental implant failure. PURPOSE: This case-control study aimed to investigate the association of genetic markers (tag SNPs) in LTF gene and clinical parameters with dental implant loss. MATERIAL AND METHODS: 278 patients, both sexes, mean age 51 years old, divided into 184 without and 94 with implant loss, were genotyped for sixteen tag SNPs, representative of the whole LTF gene. Also, clinical oral and systemic parameters were analyzed. Univariate and Multivariate Logistic Regression model were used to analyze the results (p < .05). RESULTS: No association was found between the tag SNPs and implant loss in the study population. Clinical association was found with medical treatment, hormonal reposition, edentulism, number of placed implants, plaque, calculus, and mobility. CONCLUSION: Clinical variables, but not LTF gene polymorphisms, were associated with implant loss.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".