From 1883 to 2019; Variables Influencing Body Procurement at McGill University
Bibliographic record
Abstract
McGill University has continued to maintain whole body dissection as an integral component of its medical school curriculum. To better explore the factors influencing body procurement at McGill University, records of body receipts were collected from their paper and electronic records and analyzed from 1883 to 2019. The data collected allowed for discussion on the number of bodies received at McGill University each year, the age at death of the bodies, their sex, as well as religious affiliation and language spoken at home. As bodies of the deceased have a long held and unique status in law, this information was considered in light of historical and legislative data and, in the case of religion and language, it was compared to census data for Montréal, Québec, Canada. Overall numbers of bodies procured by McGill University have varied throughout the years, with no discernable target number over time. The current body donor profile at McGill University is likely to be a male in his 70's and likely to speak French at home. However, the strong connection of the University with the Anglophone community of Montréal is reflected by the significantly higher proportion of English-speaking donors when compared to the general population of the city. In regard to legislation, it appears that the most recent legislations did not affect the rate of procurement. However, when legislations were embraced by religious institutions in 1883, there was a 261% increase in bodies sent for dissection, over the following two decades.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".