Does Systemic Chemotherapy Influence Skeletal Growth of Young Osteosarcoma Patients as a Treatment-Related Late Adverse Effect?
Bibliographic record
Abstract
The aim of this study was to investigate the influence of systemic chemotherapy on the skeletal growth of young osteosarcoma patients as a treatment-related late adverse effect. We reviewed the height data of 20 osteosarcoma patients (13 males and 7 females) aged ≤18 years. The average (±SD) age at diagnosis was 14.5 (±3.3) years. The average follow-up interval was 89.6 months. After wide resection of the affected bones, reconstruction with tumor prostheses and auto-bone grafting was carried out in 11 and 9 cases, respectively. Pearson’s correlation coefficient was calculated to evaluate the association between actual and predicted (using Paley’s multiplier method) heights. Z-scores were used to compare the initial and final heights with the Japanese national growth curve. Actual and predicted heights were correlated according to Pearson’s correlation coefficient (R = 0.503). Z-analysis showed that statistical significance (p = 0.04) was noted for the height data Z-scores of patients between ≤10 years and >10 years at the final follow-up. Systemic chemotherapy did not reduce skeletal growth in young osteosarcoma patients as a late adverse effect based on two different evaluation methods. However, patients aged ≤10 years at diagnosis may develop a short stature after systemic chemotherapy.
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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) | 0.001 | 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".