Post-mortem Examination of the Nervous System: Fresh versus Fixed
Bibliographic record
Abstract
BACKGROUND: Post-mortem examination of the nervous system is a complex task that culminates in "brain cutting". It relies on expertise in neuroanatomy, clinical neurosciences, neuroimaging and experience in order to recognise the most subtle abnormalities. Like any specialist examination in medicine, it warrants formal training, a standardised approach and optimal conditions. Revelations of aberrant tissue retention practices of a select few pathologists (e.g. Goudge, Liverpool and Alder Hey inquiries) and a motivated sociopolitical climate led some Canadian jurisdictions to impose broad restrictions on tissue retention. This raised concerns that nervous system examinations for diagnosis, education and research were at risk by limiting examinations to the fresh or incompletely fixed state. Professional experience indicates that cutting an unfixed or partly fixed brain is inferior. METHODS: To add objectivity and further insight we sought the expert opinion of a group of qualified specialists. Canadian neuropathologists were surveyed for their opinion on the relative merits of examining brains in the fresh or fully fixed state. RESULTS: A total of 14 out of 46 Canadian neuropathologists responded (30%). In the pervasive opinion of respondents, cutting and sampling a brain prior to full fixation leads to a loss of diagnostic accuracy, biosafety and academic deliverables. CONCLUSIONS: Brain cutting in the fresh state is significantly impaired along multiple dimensions of relevance to a pathologist's professional roles and obligations.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".