Bibliographic record
Abstract
Lymphomas affecting the bones of the jaws, although less frequent than carcinomas, can both present radiologically as carcinomas in addition to the more frequent "periapical-radiolucencies-of-inflammatory-origin" (PRIOs). Certainly those lymphomas arising within the maxillary alveolus have a short period of prior awareness before presentation, denoting an aggressive process. Half are provisionally diagnosed as carcinomas and the other half as PRIOs. Failure of the latter to respond to appropriate treatment, compels prompt and appropriate investigation for a malignancy. Further distinction of the malignancy into carcinoma and lymphoma is necessary, because the treatment of carcinomas is radical, achieved mainly by resection plus radiotherapy, whereas treatment of lymphomas relies on chemotherapy and in some cases, radiotherapy. The few reported cases that have been subject to cross-sectional imaging and reporting by radiologists has only appeared relatively recently. These cases reveal roles for cone-beam computer tomography, computed tomography and magnetic resonance (MR). Ultimately the diagnosis is dependant on a biopsy from the most representative area/s and the treatment plan upon the diagnosis and extent of the disease defined by the imaging.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".