What’s in a name? On fibroblast phenotype and nomenclature
Bibliographic record
Abstract
Fibroblasts have long been recognized as important stromal cells, playing key roles in synthesizing and maintaining the extracellular matrix, but historically were treated as a relatively uniform cell type. Studies in recent years have revealed a surprising level of heterogeneity of fibroblasts across tissues, and even within organs such as the skin and heart. This heterogeneity may have functional consequences, including during stress and disease. While the field has moved forward quickly to begin to address the scientific import of this heterogeneity, the descriptive language used for these cells has not kept pace, particularly when considering the phenotype changes that occur as fibroblasts convert to myofibroblasts in response to injury. We discuss here the nature and sources of the heterogeneity of fibroblasts, and review how our understanding of the complexity of the fibroblast to myofibroblast phenotype conversion has changed with increasing scrutiny. We propose that the time is opportune to reevaluate how we name and describe these cells, particularly as they transition to myofibroblasts through discrete stages. A standardized nomenclature is essential to address the confusion that currently exists in the literature as to the usage of terms like myofibroblast and the description of fibroblast phenotype changes in disease.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".