Bibliographic record
Abstract
OBJECTIVES: We report the common surgical approaches, incidence of sinus tracts, and recurrence rates of floor of mouth dysontogenic (epidermoid, dermoid, and teratoid) cysts in the pediatric population. METHODS: Data were derived from PubMed, Medline, Embase, Google Scholar, and manual searches. Three cases from the senior author's (J.P.M.) practice were included. All English-language studies consisting of floor of mouth dysontogenic cysts were included. Case reports of tongue dysontogenic cysts, mandibular dysontogenic cysts, maxillary dysontogenic cysts, and dysontogenic cysts in the neck below the hyoid bone were excluded. RESULTS: There are 198 case reports, including those presented here, of floor of mouth dysontogenic cysts. They are more common in male patients (55.1%), and the most common location is in the sublingual space (104 or 52.5%). Most floor of mouth dysontogenic cysts can be excised by an intraoral approach. There are 5 reported cases in the literature of recurrent dysontogenic cysts and 11 cases of multiple floor of mouth dysontogenic cysts. CONCLUSIONS: Floor of mouth dysontogenic cysts most commonly present in the sublingual space, and most can be excised by an intraoral approach. Multiple dysontogenic cysts often require a combination of intraoral and extraoral approaches. Recurrence of a dysontogenic cyst may be secondary to a tract not identified at the time of surgery.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".