Association between Inflammatory Markers and Local Recurrence in Patients with Giant Cell Tumor of Bone: A Preliminary Result
Bibliographic record
Abstract
Giant cell tumor of bone (GCTB) has a high local recurrence rate of approximately 20%. Systemic inflammatory markers, such as neutrophil-lymphocyte ratio (NLR), modified Glasgow prognostic score (mGPS), prognostic nutritional index (PNI), lymphocyte-monocyte ratio (LMR), platelet-lymphocyte ratio (PLR), hemoglobin (Hb), alkaline phosphatase (ALP), and lactate dehydrogenase (LDH), have been reported as prognostic markers in patients with malignant tumors. This study aimed to investigate the correlation between these markers and the local recurrence rate of GCTB. In total, 103 patients with GCTB who underwent surgery at the authors' institutions between 1993 and 2021 were included. Thirty patients experienced local recurrence. Univariate and multivariate analysis showed that tumor site, preoperative and postoperative denosumab treatment, and surgery were significantly associated with local recurrence-free survival. LDH was associated with local recurrence-free survival on univariate analysis only. NLR, mGPS, PNI, LMR, and PLR score did not correlate with the local recurrence rate. In conclusion, NLR, mGPS, PNI, LMR, PLR score, Hb, ALP, and LDH levels are not correlated with the local recurrence rate of GCTB. However, due to the small number of patients included in this study, this result should be re-evaluated in a multicenter study with a larger sample size.
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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 | 0.000 | 0.000 |
| 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".