A Multidisciplinary Evaluation Helps Identify the Antisynthetase Syndrome in Patients Presenting as Idiopathic Interstitial Pneumonia
Bibliographic record
Abstract
INTRODUCTION: Interstitial lung disease (ILD) is 1 possible manifestation of the idiopathic inflammatory myopathies (IIM). Occasionally, patients presenting with ILD are mistakenly diagnosed with idiopathic interstitial pneumonia (IIP), but after multidisciplinary evaluation, their ILD is determined to be because of antisynthetase syndrome (SynS) or myositis spectrum of disease. METHODS: We used retrospective analytic methods to identify patients with ILD evaluated at the National Jewish Health between February 2008 and August 2014 and believed initially to have IIP but ultimately diagnosed with SynS or myositis spectrum of disease. RESULTS: The cohort included 33 patients; most were white women with a mean age at presentation of 55 years. Their pulmonary physiologic impairment was moderate. In 31 cases, the ILD pattern by thoracic high-resolution computed tomography scan was nonspecific interstitial pneumonia (NSIP), organizing pneumonia (OP), or a combination of the 2. Surgical lung biopsy was performed in 21 patients; NSIP was the most common pattern. Less than one-third of the cohort had positive antinuclear antibodies. Two-thirds had positive SSA. All patients had either myositis-specific or myositis-associated autoantibody. Most had subtle extrathoracic symptoms or signs of SynS; 12 had an elevated serum creatine phosphokinase, but none had clinical evidence of myositis. None met the Peter and Bohan classification criteria for polymyositis/dermatomyositis. CONCLUSION: Among patients who present with presumed IIP, a multidisciplinary evaluation that includes the integration of clinical evaluations by rheumatologists and pulmonologists, morphologic (both histopathologic and radiographic) data, and serologic features is helpful in the detection of occult SynS or the myositis spectrum of disease.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".