Fibrous dysplasia in a Hong Kong community: the clinical and radiological features and outcomes of treatment
Bibliographic record
Abstract
OBJECTIVES: To evaluate the principal clinical and radiological features of a consecutive series of cases of fibrous dysplasia (FD) affecting a Hong Kong Chinese community and to determine their outcome by follow-up. METHODS: All cases were accompanied by appropriate radiography and were histopathologically confirmed as fibro-osseous lesions. RESULTS: The clinical and radiological presentations, differential diagnoses and outcomes of follow-up of 21 consecutive FD cases were reviewed. Females were slightly more frequently affected, but there was no gender predilection for either jaw. The mean age at first presentation was 25 years, which upon adjustment for prior awareness fell to 21 years. The main symptom was swelling (including enlargement of the jaw). The maxilla and mandible were affected in 9 and 13 cases, respectively; both jaws were affected in 1 case. One case was of McCune-Albright syndrome. The sole provisional diagnosis for all lesions was FD; most lesions affected all or almost half the affected jaw. 17 patients were followed-up for a mean of 9 years. CONCLUSIONS: This very detailed consecutive series followed up over many years, in which a number of cases recurred or were reactivated in adulthood, firmly dispells the notion that FD is a simple hamartoma in all cases. This community was quite amenable to really long-term follow-up.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".