Incidence of Total Hip and Knee Replacement in UK Patients with Ankylosing Spondylitis
Bibliographic record
Abstract
Ankylosing spondylitis (AS) is a chronic inflammatory disease of the axial skeleton manifested by back pain and progressive stiffness. However, it is not uncommon for peripheral joints to be involved; the underlying histopathology of which is characterized by subchondral bone inflammation that can result in joint erosion and eventual destruction. There have been emerging data on the need for joint replacement surgery among patients with AS1,2,3, although these data have mainly originated from cohorts of patients under hospital care or registry data and therefore may be more selective of severe disease. Our aim here was to better understand the need for joint replacement surgery in the overall AS population in the United Kingdom by using National Health Service (NHS) primary care electronic medical records to determine the incidence of total hip replacement (THR) and total knee replacement (TKR) following diagnosis of AS. We identified patients in the Clinical Practice Research Datalink (CPRD) with a first diagnosis of AS within the United Kingdom between January 1, 1995, and March 31, 2014. We excluded patients with a diagnosis of > 1 type of inflammatory arthritis. CPRD is a primary … Address correspondence to Dr. D. Prieto-Alhambra, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK. E-mail: daniel.prietoalhambra{at}ndorms.ox.ac.uk
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".