Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Activation of fibroblasts is a key event during normal tissue repair after injury and the dysregulated repair processes that result in organ fibrosis. To most researchers, fibroblasts are rather unremarkable spindle-shaped cells embedded in the fibrous collagen matrix of connective tissues and/or deemed useful to perform mechanistic studies with adherent cells in culture. For more than a century, fibroblasts escaped thorough classification due to the lack of specific markers and were treated as the leftovers after all other cells have been identified from a tissue sample. With novel cell lineage tracing and single cell transcriptomics tools, bona fide fibroblasts emerge as only one heterogeneous sub-population of a much larger group of partly overlapping cell types, including mesenchymal stromal cells, fibro-adipogenic progenitor cells, pericytes, and/or perivascular cells. All these cells are activated to contribute to tissue repair after injury and/or chronic inflammation. "Activation" can entail various functions, such as enhanced proliferation, migration, instruction of inflammatory cells, secretion of extracellular matrix proteins and organizing enzymes, and acquisition of a contractile myofibroblast phenotype. We provide our view on the fibroblastic cell types and activation states playing a role during physiological and pathological repair and their crosstalk with inflammatory macrophages. Inflammation and fibrosis of the articular synovium during rheumatoid arthritis and osteoarthritis are used as specific examples to discuss inflammatory fibroblast phenotypes. Ultimately, delineating the precursors and functional roles of activated fibroblastic cells will contribute to better and more specific intervention strategies to treat fibroproliferative and fibrocontractive disorders.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it