General histological woes: Definition and classification of tissues
Bibliographic record
Abstract
The modern view that the human body is composed of tissues and body fluids, and that there are four basic tissue types, may be a more significant departure from Artistotle's homoeomeres, and from Bichat's membranes and tissues, than commonly appreciated. The older concepts described these body parts as structural and functional parts of organs, whereas it is now commonplace to consider a tissue to be a grouping of similar cells with a variable amount of extracellular matrix. The development of the microscope as a useful tool in human anatomy shifted focus from tissues to cells and led to changes in the definition of tissue and the classification of tissues. Not all of these changes have been consistent with observable facts: many tissues contain diverse cell types, not all "connective tissues" are proper connective tissues, and some specialized tissues are not easily classified as subtypes of one of the four basic types. Here we propose corrective measures, including re-recognition of compound tissues, cataloging all adult human tissue types, and increasing the emphasis on function during the construction of a complete taxonomy of human adult tissues. Specific problems in the current scheme and a preliminary reclassification of human adult tissues are discussed.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".