Clinical and radiological characteristics of oro‐antral communications/fistulae due to implant dentistry procedures: A cross‐sectional retrospective study
Bibliographic record
Abstract
OBJECTIVES: Assess the unique clinical and radiological sequelae following oro-antral communications/fistulae (OAC/OAF) due to implant dentistry vs other etiologies. MATERIALS AND METHODS: A structured form served to collect data from medical records. All consecutive patients who underwent surgical closure of OACs/OAFs between 2003 and 2020, at a single center were included. Demographic, radiological, clinical, operative and postoperative characteristics were collected. The differences between groups (cases with implant dentistry etiology [IDE] vs cases with other etiologies) were assessed statistically. RESULTS: Data were gathered from 121 cases. The findings show that IDE cases were more likely to be of older age (OR = 1.07, CI [1.02, 1.13] P = .02); to have a foreign body in the maxillary sinus (OR = 21.04, CI [4.34, 114.92] P < .01); to have fluid passage (OR = 11.40, CI [1.87, 118.73] P = .02) and purulent discharge through the fistula (OR = 3.52, CI [0.86, 16.34] P = .09). CONCLUSIONS: Clinical and radiological sequelae due to OACs/OAFs secondary to implant dentistry procedures are more severe compared to other etiologies. The suggested pathogenesis is foreign body reaction. Early and accurate diagnosis of the foreign body location, followed by its early removal is recommended.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".