Expressions and clinical significance of factors related to giant cell tumor of bone.
Bibliographic record
Abstract
BACKGROUND: Giant cell tumor of bone (GCTB) is a relatively rare tumor of bone, characterized by numerous multinucleated cells, severe osteolysis, and local recurrence. PURPOSE: To explore the role of S-phase kinase-interacting protein 2 (Skp2), cyclin-dependent kinase inhibitor p27, and the transcription factor E2F-1 expression in the development of GCTB, and the relationship of expression of these proteins with tumor recurrence. METHODS: Forty-four patients with GCTB were selected and demographic and clinical data were collected. The levels of Skp2, p27, and E2F-1 protein expression were immunohistochemically assessed in surgical specimens. RESULTS: Skp2, p27, and E2F-1 proteins were detected in the nuclei of mononuclear stromal cells. Positive Skp2 expression was observed in 66% (29/44) of GCTB patient samples, and positive p27 expression was found in 39% (17/44) of samples. Within almost all GCTB patients, there was an inverse correlation between Skp2- and p27-positive tumor cells. Positive expression of E2F-1 was present in 28 of 44 (64%) patients. In addition, expression of skp2 and p27, infiltration of soft tissues, and surgical operation were significantly associated with recurrence in patients with GCTB. CONCLUSION: The immunohistochemical assessment of Skp2, p27 and E2F-1 may be useful in the diagnosis of GCTB and prediction of its prognosis.
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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 | 0.000 | 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".