Bibliographic record
Abstract
Oscar is a 9 year old male castrated Golden Retriever Dog who presented for an oral mass located between the left mandibular premolar 3 and premolar 4, which was previously diagnosed by the rDVM as an acanthomatous epulis. Physical examinations did not find any major abnormalities other than the oral mass. Oral masses in dogs are most commonly squamous cell carcinoma, melanoma, or fibrosarcoma but also include chondrosarcoma, osteosarcoma, and the epulides. Original incisional biopsy reported chondrosarcoma, although later surgical biopsy revealed a chondroblastic osteosarcoma. Dental radiographs and CT scan revealed a large aggressive tumor causing boney lysis of the mandible. A mandibulectomy was performed. This paper discusses intraoperative and postoperative complications associated with mandibulectomy. The major intraoperative complication is hemorrhage from the mandibular artery. Postoperative complications include ranula formation, short term anorexia, dehiscence, instability of the remaining mandibles, and palatal contact ulceration resulting from the mandibular canine tooth after medial mandibular drift.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".