Facial disfigurement due to osteitis fibrosa cystica or brown tumor from secondary hyperparathyroidism in patients on dialysis: A systematic review and an illustrative case report
Bibliographic record
Abstract
Osteitis fibrosa cystica (OFC) is the most frequent type of osseous change in renal osteodystrophy affecting the majority of dialysis patients. Brown tumors are a severe form of OFC. The involvement of the craniofacial skeleton causing facial disfigurement in patients on dialysis appears to be limited to case reports. After searching PubMed, we performed a systematic review of 127 cases with a severe form of OFC resulting in a facial disfigurement to understand possible determinants for this condition. We found that since the first published case in 1974, and after a peak in 1996, there appears to be an increase in published reported cases. Only 27.6% of these cases were published in nephrology journals. The most common region for reported cases was North America. Mean age of these patients was 31.2 years with a mean dialysis duration of 7 years. Almost 67% were women, and almost all were on hemodialysis. The disease tended to most commonly localize to the maxilla (73.2%) and mandible (57.5%). As part of the treatment, 59% of patients had a parathyroidectomy. More than one-third (35.4%) had symptomatic improvement at follow-up. Mean follow-up was 1.6 years. Clinicians should be aware of this clinical presentation of a severe form of OFC and/or brown tumors. Timely diagnosis and intervention may help to prevent or decrease destructive bone changes and reduce negative psychological consequences of facial disfigurement.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".