Cause and Effect Considerations in Diagnostic Pathology and Pathology Phenotyping of Genetically Engineered Mice (GEM)
Bibliographic record
Abstract
Over the next several decades, biology is embarking on its most ambitious project yet: to annotate the human genome functionally, prioritizing and focusing on those genes relevant to development and disease. Model systems are fundamental prerequisites for this task, and genetically engineered mice (GEM) are by far the most accessible mammalian system because of their anatomical, physiological, and genetic similarity to humans. The scientific utility of GEM has become commonplace since the technology to produce them was established in the early 1980s. Conceptually, however, an efficiently coordinated high-throughput approach that permits correlation between newly discovered genes, functional properties of their protein products, and biological relevance of these products as drug targets has yet to be established. The discipline of veterinary anatomical pathology (hereafter referred to as pathology) is not immune to this requirement for evolution and adaptation, and to address relationships and tissue consequences between tens of thousands of genes and their cognate proteins, novel interdisciplinary technologies and approaches must emerge. Although many of the techniques of pathology are well established, in the context of pathology's contribution to functional annotation of the genome, several conceptually important and unresolved issues remain to be addressed. While an ever-increasing arsenal of genetic and molecular tool-sets are available to evaluate and understand the function of genes and their pathophysiological mechanisms, pathology will continue to play an essential role in confirming cause and effect relationships of gene function in development and disease. This role will continue to be dependent on keen observation, a systematic but disciplined approach, expert knowledge of strain-dependent anatomical differences and incidental lesions, and relevant tissue-based evidence. Miniaturization and high-throughput adaptation of these methods must also continue so that they can complement parallel phenotyping efforts, provide pathology-based data in pace with concurrent phenotyping efforts, and continue to find new utility in the collective effort of functional annotation.
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 | 0.027 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".