Canadian Association of Neuropathologists Association canadienne des neuropathologistes
Bibliographic record
Abstract
The Canadian Association of Neuropathologist – Association canadienne des neuropathologistes (CANP-ACNP) held their 60th annual meeting via Zoom from October 15th to 17th, 2020, under the leadership of Dr. Peter Gould, President of the CANP-ACNP, Dr. Peter Schutz, Secretary Treasurer of the CANP-ACNP, and with technical support from CANP administrators Heather Dow and Colleen Fifield. The academic program comprised 10 scientific abstracts, 10 unknown cases, a symposium on Neuromuscular Pathology, and a Neuropathology Practice lecture by Dr. Emina Torlakovic entitled The Fall and Rise of Immunohistochemistry. The interactive forum on Neuropathology Practice was moderated by Dr. Gould and Dr. Keith and focused on forensic neuropathology in Canada. The Jerzy Olszewski Lecture was delivered by Dr. Alex Rajput and Dr. Ali Rajput on Neuropathology and Saskatchewan Movement Disorders Program. Digital pathology images from the 10 unknown cases are available for viewing online ( www.canp.ca ) thanks to the CANP webmaster Dr. Andrew Gao. The Presidential Symposium 2020 on Neuromuscular Pathology featured the David Robertson Lecture given by Dr. Werner Stenzel entitled The Inflammatory Myopathies – Essential Role of Muscle Biopsies for Precise Diagnosis and the Gordon Mathieson Lecture delivered by Dr. Benjamin Ellezam on The Role of Muscle Biopsy in the Molecular Era: Practical Pointers in Diagnostic Pathology of Inherited Myopathies. The program was completed by Dr. Robert Schmidt’s presentation on Perspectives on the Evaluation of Nerve Biopsies, and Dr. Jodi Warman Chardon’s presentation on MRI & Diagnosis of Muscle Disease. The Mary Tom Award for best clinical science presentation by a trainee went to Dr. Noor Alsafwani (Supervisor Dr. A. Gao), and the Morrison H. Finlayson Award for best basic science presentation by a trainee was won by Dr. Delaney Cosma (Supervisor Dr. R. Hammond). The following abstracts were presented at the 60th annual meeting of the Canadian Association of Neuropathologists – Association candienne des neuropathologistes (CANP-ACNP) in October 2020.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.153 | 0.051 |
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".