Three-dimensional assessments are necessary to determine the true, spatially resolved composition of tissues
Bibliographic record
Abstract
Methods for spatially resolved cellular profiling of tissue sections enable in-depth study of inter- and intra-sample heterogeneity but often profile small regions, requiring evaluation of many samples to compensate for limited assessment. Recent advances in three-dimensional (3D) tissue mapping offer deeper insights; however, attempts to quantify the information gained in transitioning to 3D remains limited. Here, to compare inter- and intra-sample tissue heterogeneity, we analyze >100 pancreas samples as cores, whole-slide images (WSIs), and cm 3 -sized 3D samples. We show that tens of WSIs and hundreds of tissue microarrays are needed to approximate the compositional tissue heterogeneity of tumors. Additionally, spatial correlations of pancreatic structures decay significantly within microns, demonstrating that isolated two-dimensional (2D) sections poorly represent their surroundings. Through 3D simulations, we determined the number of slides necessary to accurately measure tumor burden. These results quantify the power of 3D mapping and establish sampling methods for biological studies prioritizing composition or incidence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".