Canadian Association of Neuropathologists L’Association Canadienne des Neuropathologistes
Bibliographic record
Abstract
The Canadian Association of Neuropathologist – L’ Association Canadienne de Neuropathologistes (CANP-ACNP) held their 59th annual meeting at the Delta Kingston Waterfront from October 23rd to 26th, 2019, under the leadership of Dr. Peter Gould, President of the CANP-ACNP, Dr. Julia Keith, Secretary Treasurer of the CANP-ACNP, and Dr. John Rossiter, local organizer. The annual banquet was held at River Mill Restaurant in Kingston. The academic program comprised 14 Abstracts, 14 unknown cases, a Symposium on Neurodegenerative Neuropathology, and a Neuropathology Practice lecture by Dr. Gerard Jansen entitled CJD, CJD Surveillance, and Occupational Risk. Can worms ever be re-canned? The interactive forum on Neuropathology Practice was moderated by Dr. Gould and Dr. Keith and focused on safety around autopsy diagnosis of CJD, the Neuropathology workforce analysis in Canada 2019 presented by Dr. Patrick Shannon, and accreditation of neuropathology laboratories in Canada. Digital pathology images from the 14 unknown cases are available for viewing online ( www.canp.ca ) thanks to the CANP webmaster Dr. Jason Karamchandani. The Presidential Symposium 2019 on Neurodegenerative Neuropathology featured the Jerry Olszewski Lecture given by Dr. Douglas Munoz on Using eye tracking to identify behavioural biomarkers of neurodegeneration, the David Robertson lecture given by Dr. Tom Beach on Staging systems for Lewy body diseases, and the Gordon Mathieson lecture given by Dr. Ian Mackenzie on C9orf72: FTD, ALS and beyond. The program was completed Dr. Gabor Kovacs’ presentation on Tau pathologies in the aging brain and Dr. Carmela Tartaglia’s presentation on Dementia; the times they are a changing. The award for best clinical science presentation by a trainee (Dr. Mary Tom Award) in 2019 went to Dr. Suzy Kosteniuk (Supervisor Dr. Lothar Resch), and the award for best basic science presentation by a trainee (Dr. Morrison H. Finlayson Award) was won by Hoang D. Nguyen (Supervisor Dr. Maxime Richer). The following abstracts were presented at the 59th annual meeting of the Canadian Association of Neuropathologists – Association Candienne des Neuropathologistes (CANP-ACNP) in October 2019.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.129 | 0.041 |
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".