Incidental findings of implant complications on postimplantation CBCTs: A cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: Survival rates of dental implants are reported to be very high and seem to indicate minimal complications related to dental implants. PURPOSE: The aim of this report was to evaluate in a cross-sectional study the prevalence of implant positioning complications as appears in postimplantation Cone-Beam Computerized Tomography (CBCT) in two of the major imaging facilities in Bucharest, Romania. METHODS: Demographic and implant data was collected from two of the three main CBCT facilities in Bucharest, Romania. All postimplantation CBCT imaging were assessed and evaluated for the presence of different implant positioning related complications. Data were entered into Excel spreadsheet and analyzed statistically. RESULTS: Of the 2323 CBCT's that were analyzed, a total of 160 (6.89%) presented with implant positioning related complications. Out of those, 62 cases revealed penetration of the implant to adjacent anatomic structure. More specifically, there were 21 instances of sinus penetration, 19 instances of nasal cavity penetration, 9 instances of inferior alveolar canal penetration, and 13 instances of lingual plate perforations. There were also 15 cases of adjacent tooth injury noted. CONCLUSIONS: Despite the popularity of dental implants, the surgical placement of these implants is not a riskless procedure. Implant mal-positioning might be life-threatening and can lead to serious bleeding, airway obstruction, and unnecessary postoperative surgeries. Complications of dental implants are not obsolete and dental implant associated problems may not be apparent immediately. Surgeons must have proper training and use evidenced-based treatment planning in order to prevent dental implant complications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".