Genetic screening algorithm for inflammatory back pain
Bibliographic record
Abstract
Objective: To develop a single nucleotide polymorphism (SNP) based genetic-based algorithm among patients with low back pain to screen for axial spondyloarthritis (SpA). Methods: An 18-plex genetic assay was designed using a MassARRAY, consisting of SNPs associated with ankylosing spondylitis (AS), psoriasis, inflammatory bowel disease (IBD) and uveitis. 1172 AS cases and 848 controls have been analyzed over two cohorts. A machine learning algorithm was created using a J48/C4.5 decision tree model; the first decision was human leukocyte antigen B 27 (HLA-B*27) status. The initial algorithm was validated in an independent cohort. The discovery and validation cohorts were then combined and the final genetic-based screening algorithm was weighted. Results: The SNP based algorithm that included HLA-B*27 positivity had a precision, specificity and sensitivity of; 0.83, 0.83, and 0.80, respectively which is higher than the current HLA-B*27 based Assessment of Spondyloarthritis International Society (ASAS) classification criteria. The SNP based algorithm that included HLA-B*27 negativity had a precision, specificity and sensitivity of, 0.58, 0.32, and 0.69, respectively. Conclusions: This genetic screening algorithm is inexpensive, out performs the clinical arm of the current ASAS classification criteria and can potentially lead to earlier detection of axial SpA.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".