A case of mandibular arteriovenous malformation requiring frequent embolization
Bibliographic record
Abstract
We present a case report of recurrent transvenous and transarterial embolization for mandibular arteriovenous malformation. An 11-year-old female patient experienced hemorrhaging from the left mandibular gingiva since October 2018. In mid-January 2019, she was urgently referred to a local physician owing to persistent bleeding from the same area. Computed tomography revealed osteolytic alterations in the left mandible. The patient was referred to our center for accurate assessment. Suspecting arteriovenous malformation, magnetic resonance imaging was performed, revealing heightened signal intensity in T2-weighted images and decreased signal intensity on T1-weighted images surrounding the left mandible.The patient received transvenous coil embolization and transarterial embolization utilizing cyanoacrylate material. In October 2019, abscess and cutaneous fistula formation were noted due to embolic infection. The introduction artery was treated with transarterial embolization using a gelatin sponge, and mandibular curettage, including the extraction of teeth 33-35, was carried out. Afterward, residual inflammation was detected around the coil in the mandibular molar region on the left side. Therefore, removal of the embolic material and the extraction of teeth 36-38 were performed. Arterial bleeding was identified intraoperatively, requiring transarterial embolization(TAE). At present, the patient exhibits no evidence of lesion recurrence.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".