Analysis of a molecular genetic neuro-oncology study with partially biased selection
Bibliographic record
Abstract
Oligodendrogliomas are a common variant of malignant brain tumors, and are unique for their relative sensitivity to chemotherapy and better prognosis. For these reasons, the identification of an objective oligodendroglial marker has been a long sought-after goal in the field of neuro-oncology. To this end, 75 patients who received chemotherapy at the London Regional Cancer Centre between 1984 and 1999 were studied (Ino et al., Clinical Cancer Research, 7, 839-845, 2001). Of these 75 patients, 50 were initially treated with chemotherapy (the current practice) and comprise a population-based sample. The remaining 25 patients were initially treated with radiation and were included in the study only because their tumor recurred, at which time they received chemotherapy. Because this group of 25 patients included neither those radiation patients whose tumors never recurred nor those radiation patients whose tumors recurred but were not treated with chemotherapy, issues of selection bias were of concern. For this reason, the initial analysis of these data included only the 50 population-based patients. This was unsatisfying given the rarity of this disease and of genetic information on this disease and led us to question whether we could undertake an analysis that includes all of the patients. Here we examine approaches for utilizing the entire study population, as well as the assumptions required for doing so. We illustrate that there are both costs and benefits to using the 25 selected patients.
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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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".