Computed tomography of fibrous dysplasia
Bibliographic record
Abstract
OBJECTIVE: Fibrous dysplasia (FD) is an uncommon, but important lesion affecting the jaws. The aim of this study was to reveal its presentation on computed tomography (CT) in a consecutive series of cases. METHODS: The files of the Department of Oral and Maxillofacial Surgery of Hong Kong University between 1989 and 2000 were reviewed for cases of FD. RESULTS: Of the ten cases investigated by CT, two were rejected because of extensive biopsy or surgery before the CT investigation. Seven of the remaining eight cases were Chinese and one was Indian. The mandible and maxilla were equally affected. The cortex was generally intact, except when adjacent to the teeth in the maxilla. The margins were generally poorly-defined, but well-defined on at least some sections of each maxillary case. Five cases were extensive, affecting or nearly affecting the whole hemi-mandible or hemi-maxilla to the midline. All cases displayed expansion, which was fusiform in the mandible and an enlargement of the normal contour in the maxilla. The maxillary antrum was completely obturated in three maxillary cases; one displayed a rounded dome-shaped lesion more suggestive of a benign neoplasm. The orbital floor was displaced in three cases; one of those cases presented with proptosis. All maxillary cases extended back to the pterygoid process, but did not displace it. The "bone windows" of eight cases generally displayed a "ground-glass" pattern; one also displayed cyst-like radiolucencies. The soft tissue window, which depicts mineralized tissue as "white", showed that five cases were completely mineralized. CONCLUSION: CT can be used to determine the extent, specific dimensions and radiodensity of FD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".