Collaborating to advance interdisciplinary care for individuals with arthrogryposis
Bibliographic record
Abstract
This Special Issue on Interdisciplinary Care in Arthrogryposis highlights a collection of articles spanning topics in interdisciplinary care, genetic discoveries, and clinical research. An international group of clinicians and researchers from various backgrounds who attended the "3rd International Symposium on Arthrogryposis", held in Philadelphia, September 24-26, 2018, were invited to contribute to this issue. The goal of the 2018 Symposium and of this Special Issue is to provide momentum to advancing evidence-based practice and research in arthrogryposis, by working collaboratively with adults and families of children with arthrogryposis, clinicians, and researchers. The contents of this issue cover a range of topics from defining and classifying arthrogryposis multiplex congenita to early detection, rehabilitation, and orthopedic management, advances in genetic pathways, patient registries, autopsy guidelines, and research findings in the pediatric and adult populations with arthrogryposis. We hope that this issue provides an overview as well as new knowledge on arthrogryposis to generate more conversations at the international level, and advance care and research for individuals with arthrogryposis.
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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".