Endoscopic removal of ectopic sinonasal teeth: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Ectopic sinonasal teeth are uncommon. The classic approach to removal of such foreign bodies was the Caldwell-Luc. In recent years however, endoscopic approaches have become increasingly utilized. Despite this, there is a dearth of literature and consensus regarding the endoscopic removal of ectopic sinonasal teeth. As such, we conducted a systematic review on all cases of endoscopic removal of ectopic sinonasal teeth in the literature. With an understanding of the literature, clinical and technical decision making for patients with this pathology may be elucidated. METHODS: Systematic review of the Ovid Medline, EMBASE Classic and Pubmed databases were conducted using PRISMA guidelines. RESULTS: Our search identified 100 articles. Final inclusion consisted of 23 studies with a total of 27 patient cases. The majority of the patients included were male (70.4%) with a mean age of 27.06 years. Patients presented with a multitude of symptoms, with nasal obstruction (48.14%), rhinorrhea (22.2%), facial pain (22.2%) and epistaxis (22.2%) being most common. Surgeons mostly reported using a 0° endoscope (22.2%) and performing a maxillary antrostomy/uncinectomy (37%) and simple extraction under general anesthetic (41%). CONCLUSIONS: This systematic review analyzed important epidemiological, clinical and technical information regarding patients with endoscopic removal of sinonasal ectopic teeth. Further research is needed to promote implementation of such data into clinical practice.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".