Standardization of postmortem human brainstem along the rostrocaudal axis to accommodate for heterogeneity in samples
Bibliographic record
Abstract
Human postmortem brain tissues provide an indispensable resource that is crucial for the understanding of neurological conditions, whether related to pathology subtype, burden, distribution or cell-type specificity. Pathology staging protocols provide guidelines for standardized sampling of brain tissues, but cover only a subset of regions affected by pathologies. Thus, to study how various neuropathologies and cell types in highly specialized circuit nodes correlate with functions specifically served by these nodes, additional protocols are necessary. This especially applies to brainstem tissues due to the small dimension of regions of interest and interindividual variability of specimens, whether due to procurement or intrinsic differences. Here we systematically assessed factors contributing to heterogeneity in the length of whole brainstem samples and then presented a standardized approach to reproducibly assign rostrocaudal levels, with standardization relying upon readily identifiable internal landmarks. We validated this approach using postmortem MRI imaging. Standardized brainstem length correlated positively with subject height and negatively with subject age of death. By providing a reference series, reproducible levels can be assigned to individual histological sections or MRI images, i.e. when full brainstem specimens are not available and irrespective of platform, promoting reproducibility.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".