Update on the national survey on molecular diagnostics in CNS tumors
Bibliographic record
Abstract
There have been significant changes in the diagnostic criteria for diffuse gliomas in the 2016 WHO CNS tumor classification, with the incorporation of molecular criteria into a number of definitions. This has placed a greater emphasis on the availability of key immunohistochemical and molecular tests. In order to determine the effect that these changes have had on neuropathology practice and the access of different centres to these tests, we designed a survey that was sent to all members of the Canadian Association of Neuropathology member list in the fall of 2017. This survey asked a number of questions relating to the approach to glioma diagnosis, immunohistochemical/molecular test ordering patterns, in-house test availability, and need to send out for testing. In this presentation we will present preliminary results from this survey, with a focus on institutional testing capabilities. This provides a valuable resource that could ultimately need to a national database of immunohistochemical and molecular test availability for each neuropathology centre. LEARNING OBJECTIVES This presentation will enable the learner to: 1. Review the key molecular markers in the diagnosis of adult gliomas and methods of testing for them 2. Discuss the effect that the 2016 WHO CNS tumor update has had on clinical practice in Canada
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".