1. Traditional and Electronic Ki-67 Quantitation in Oligodendrogliomas
Bibliographic record
Abstract
The Ki-67 proliferative index has become a useful, objective, immunohistochemical tool that can aid in grading and prognostication for patients with oligodendrogliomas. Previous studies have described the prognostic significance of the Ki-67 index for such patients. According to the WHO classification of tumors of the central nervous system (2007) ”mitotic activity is low in WHO grade II oligodendroglioma, and labeling indices for proliferation markers are accordingly low, usually below 5%”. Furthermore, the predictive value of the Ki-67 index appears to be independent of age, tumor site, and histological grade. What is less well described is the relative accuracy of traditional vs. semi-automated methods of enumeration for a test where small differences can influence grading, prognosis and treatment. Tang et al. (2012), studying gastroenteropancreatic neuroendocrine tumours, found high concordance between two semi-automated methods for Ki-67 quantitation whereas “eyeballed estimates” were far less reliable. We will compare the reported proliferative index estimates to those calculated by digital image analysis of 35 recent oligodendrogliomas from the LHSC Pathology archives.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".