Bibliographic record
Abstract
Improving treatment options for people with ankylosing spondylitisAnkylosing spondylitis (AS) is a serious form of spinal arthritis.Unlike most forms of arthritis, which generally affect older people, AS tends to affect people aged 15 to 45 years old.At the Schroeder Arthritis Institute and the University of Toronto in Canada, rheumatologist Dr Nigil Haroon is improving treatment options for AS and preventing the disease from causing lifelong, disabling symptoms.Rheumatology PROFILE Talk like a ... rheumatologist Ankylosing spondylitis (AS) -a long-term condition in which the spine and other areas of the body become inflamed Arthritis -a condition causing painful inflammation of the joints Cytokine -a type of protein released by immune cells which serves as a messenger and can also cause inflammation Immune system -a network of biological systems which protect an organism from disease Inflammation -a defence mechanism that the immune system uses against infections and diseases Major histocompatibility complex (MHC) -a set of genes that code for cell-surface proteins that are vital to the immune response Rheumatology -the branch of medicine that investigates diseases which cause inflammation in the bones, muscles, joints or organs RNA sequencing -a laboratory technique that can detect and analyse RNA molecules within cells
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.009 |
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".