Proliferative Activity (Ki‐67 Expression) and Outcome in High GradeOsteosarcoma: A Study of 27 Cases
Bibliographic record
Abstract
Purpose. Although pre-operative chemotherapy has improved the prognosis for individuals with osteosarcoma, approximately 40% of patients will die of their disease.The aim of this study was to quantitate proliferative activity in high grade osteosarcomas and to determine whether proliferation is a prognostic factor.Patients. The study consisted of 27 patients with high grade non-metastatic osteosarcoma at various sites for whom pre-operative biopsies and resection specimens were available for review. All patients were treated similarly and had at least 24 months' follow-up from the date of diagnosis.Methods. Proliferative activity (Ki-67 expression) was examined in the diagnostic biopsies immunohistochemically using the MIB-1 antibody. Proliferation was quantitated in two ways; (1) the number of immunopositive cells was counted manually using an ocular grid; or (2) the percentage of immunopositive nuclear area was assessed using morphometric image analysis. Proliferative index was evaluated in relation to patient outcome.Results. Proliferative activity was seen in all biopsies.The median proliferative index as determined by counting cells was 24% (mean of 27%, range of 7-61%) and by image analysis was 2% (mean 3%, range 0.32-8.4).The correlation between MIB-1 proliferation indices determined either by image analysis methodology or manual cell counting was high (Spearman's rho=0.79). Proliferative index did not appear to predict either disease-free or overall survival.Discussion. Tumor proliferation does not appear to be prognostic for high grade osteosarcomas.Whether assessment of this feature in conjunction with other tumor characteristics might be prognostic requires further study.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".