Bibliographic record
Abstract
Findings from papers published by key speakers at the 2007 Fascia Research Congress are presented in preparation for the second congress, October 2009, in Amsterdam.The role of fascia is demonstrated in new scientific findings in mechanotransduction between the cytoskeletal structure and the extracellular matrix, and its implications for health and disease.the presence of contractile cells (myofibroblasts) within the fascial fabric. Clinicians are interested in their role in creating contractile tonus in the fascial fabric-how myofibroblasts form, how they are activated, and their influence on passive muscle tonus.the biomechanical properties of fascial tissues: creep, relaxation, hysteresis, effect of sustained spinal flexion on lumbar tissues, strain-induced hydration changes, myofascial manipulation, and fascial viscoelastic deformation. These properties underlie the response of these tissues to therapy.how fascia is innervated, and how proprioception and pain are created, detected, and modulated by the spinal cord and the rest of the nervous system.forms of mechanical signaling within the fascial matrix, such as the tugging in the collagen matrix created by twisting acupuncture needles.new techniques for measurement of fascial motion in living tissue.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.297 | 0.194 |
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".