GIANT CELL TUMOR OF THE DISTAL RADIUS: FACTORS ASSOCIATED WITH LOCAL RECURRENCE
Bibliographic record
Abstract
Objectives: To assess patient and tumor characteristics and treatment outcomes, focusing on local recurrence rates based on treatment type. Methods: This is a retrospective review of cases of GCTB of the distal radius, identified from the databases of 74 patients in Brazilian institutions specializing in musculoskeletal tumor treatment. Data were collected from electronic and paper medical records by 18 centers between 1989 and 2021. Variables included demographic data, clinical presentation, treatment-related factors, and primary outcome (local recurrence rate). Results: Among the 74 patients in the study, the mean age at diagnosis was 32.6 years, with a slight female predominance. Pathological fractures on presentation were observed in 15.7% of patients, and pulmonary metastasis in 1.4%. Treatment approaches were divided equally between intralesional curettage and en bloc resection. The overall local recurrence rate was 25.7% and was higher in patients treated with intralesional curettage (35.1%) compared to resection (16.2%). Conclusions: The study confirms high recurrence risk with intralesional curettage, emphasizing the need for standardized protocols and improved surgical techniques to reduce recurrence rates and enhance outcomes for distal radius GCTB patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".