Patterns of Outpatient Phecodes Predating the Diagnosis of Systemic Lupus Erythematosus in Taiwanese Women
Bibliographic record
Abstract
Shortening the time to diagnosis and initiating early treatment are imperative to improve outcomes in patients with systemic lupus erythematosus (SLE). The aim of this case-control study, based on the data from the Taiwan's National Health Insurance Research Database (NHIRD), was to investigate the patterns of diagnoses of disease phenotypes in female patients with SLE up to eight years prior to its definitive diagnosis. The 547 cases were selected from the 2000-2012 NHIRD catastrophic illness datafile and frequency-matched with 2188 controls. The primary diagnosis based on the first ICD-9-CM code for each outpatient visit was converted to Phecodes. Separate regression models, based on least absolute shrinkage and selection operator (lasso) regularization, with seven different lag periods from 1-2 to 7-8 years, were conducted. Results showed that SLE was associated with 46 disease phenotypes in a lag period of 2-3 years, but fewer in other lag periods. A number of SLE-associated disease phenotypes, such as primary thrombocytopenia, thyroid diseases, Raynaud's syndrome, renal disease, and several infectious diseases, occurred mainly in the first few years prior to SLE diagnosis. In conclusion, SLE should be suspected when the disease phenotypes identified in the present study occurred concomitantly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".