The article opens with a composite scenario that will feel familiar to many rheumatologists. A 40-year-old woman is referred for possible lupus after endorsing diffuse joint and muscle pain, headaches, fatigue, dry eyes and mouth, gastrointestinal distress, cognitive fog, and non-restorative sleep on a standardized review of systems. Her physical examination is entirely unremarkable: no synovitis, rash, ulcers, alopecia, edema, or neurologic deficits. Her antinuclear antibody titer is 1:160 with a dense fine speckled pattern, but antibodies to extractable nuclear antigens and double-stranded DNA are negative, complement levels are normal, and inflammatory markers are not elevated. Despite never meeting classification criteria for systemic lupus erythematosus, she carries the diagnosis in her record, has received hydroxychloroquine and repeated courses of glucocorticoids for flares, and has been counseled to delay pregnancy because of supposed disease activity. Years later, the label persists even after the medication is stopped for lack of benefit, and she has never been evaluated for or educated about fibromyalgia.

Chatterjee traces part of the problem to how modern medicine documents symptoms. Evaluation and Management guidelines introduced by the Centers for Medicare and Medicaid Services in 1995 and 1997 incentivized exhaustive checklist documentation over concise diagnostic synthesis, a shift the American College of Physicians later criticized for embedding clinically uninformative phrases such as a negative 10-point review of systems into routine notes purely to satisfy billing requirements. Within this environment of note bloat, the review of systems evolved from a focused diagnostic tool into an indiscriminate symptom inventory that can escalate concern for autoimmune disease. Yet autoimmune rheumatic diseases rarely announce themselves through diffuse symptom accumulation alone. Accurate diagnosis depends on coherent clinical phenotypes built from objective findings, such as clinical synovitis, characteristic rashes, Raynaud phenomenon, serositis, cytopenias, glomerulonephritis, inflammatory myopathy, or biopsy-proven organ involvement, accompanied by disease-specific autoantibodies.

The statistical core of the argument is Bayesian. Systemic autoimmune rheumatic diseases are collectively uncommon in the general population, whereas fibromyalgia and related central sensitization syndromes affect roughly 2 to 4 percent of adults. In a low-prevalence setting, the pretest probability of autoimmune disease is low even among highly symptomatic patients, and adding more non-specific findings does little to move it. Fatigue, diffuse pain, sleep disturbance, paresthesias, and cognitive complaints are intentionally sensitive but weakly specific symptoms that occur frequently in the absence of autoimmune disease, so each additional low-specificity finding contributes negligible diagnostic information and may obscure meaningful signals. By contrast, objective inflammatory and organ-specific findings substantially increase post-test probability. Failing to account for these base rates, the author contends, predictably produces diagnostic error.

Several well-described cognitive biases amplify the problem. Availability bias raises suspicion for autoimmune disease because these conditions are memorable, complex, and emphasized during training. Base-rate neglect leads clinicians to overestimate the likelihood of rare diseases. Anchoring bias occurs when an early finding, particularly a positive antinuclear antibody, dominates subsequent reasoning even as contradictory evidence accumulates. Once an autoimmune label is introduced, diagnostic momentum sustains it: subsequent clinicians inherit the diagnosis, interpret new symptoms through that lens, and hesitate to reverse course. Electronic health records and templated documentation perpetuate diagnoses long after their evidentiary basis has eroded, with problem-list clutter and indiscriminate review-of-systems templates reinforcing the appearance of chronic multisystem disease. The author also acknowledges that these errors are psychologically compelling, because both clinicians and patients often seek explanations proportional to the magnitude of suffering, and a multisystem autoimmune label can feel validating even when it is inaccurate.

Antinuclear antibody testing illustrates how bias and inappropriate test utilization intersect. Although the test is highly sensitive across autoimmune rheumatic diseases, it lacks specificity and performs poorly as a screening tool in low-prevalence populations. Low-titer antinuclear antibody, at 1:80 or below by indirect immunofluorescence, is present in 14 to 25 percent of healthy individuals and 10 to 12 percent of patients with fibromyalgia. Outside appropriate clinical contexts, the positive predictive value of the test for lupus is often in the single digits. When testing is ordered in low-probability settings, a positive result frequently triggers cascades of additional serologic work, incidental abnormalities, patient anxiety, and provisional labels such as possible lupus or undifferentiated connective tissue disease. Autoantibodies alone do not establish causality and may coexist with symptoms arising from entirely different processes. The test does retain real utility through its high negative predictive value, approaching 98 percent for excluding lupus, reflecting sensitivity of roughly 95 percent for systemic lupus erythematosus and systemic sclerosis and 85 to 90 percent for Sjögren syndrome and mixed connective tissue disease.

Central sensitization syndromes complicate this picture because they produce real, often disabling symptoms without consistent structural or inflammatory correlates. The absence of a definitive biomarker can create discomfort around diagnostic uncertainty, prompting clinicians to substitute non-specific serologic abnormalities as explanatory anchors. Chatterjee argues that diagnosing fibromyalgia should be viewed not as diagnostic failure but as a conclusion grounded in probability, pattern recognition, and outcome-based evidence, and that recognizing symptom amplification rather than inflammation is a learned clinical skill requiring deliberate teaching. Social determinants of health, including chronic stress, early-life adversity, repetitive occupational strain, nutritional deficiencies, and socioeconomic disadvantage, can amplify pain processing and produce diffuse symptoms resembling fibromyalgia, broadening diagnostic reasoning beyond autoimmune paradigms. Fibromyalgia also commonly coexists with confirmed autoimmune disease, occurring in about 25 percent of rheumatoid arthritis, 30 percent of lupus, and up to 50 percent of primary Sjögren’s syndrome cases, where its symptom burden can be mistaken for inflammatory flare.

Fear of missing early lupus often drives ongoing surveillance, but longitudinal data are reassuring. In patients with fibromyalgia, including those with positive antinuclear antibodies, the risk of developing lupus is approximately 0.0027 percent per year, similar to the general population, and low-titer antinuclear antibody does not predict future autoimmune rheumatic disease. Among antibody-positive individuals without established disease, progression is uncommon and is driven by evolving objective clinical features, disease-specific autoantibodies, and interferon signatures rather than antibody positivity alone. Serial autoantibody testing adds little value in the absence of new clinical findings. The author notes that some diseases, including the spondyloarthritis spectrum, polymyalgia rheumatica, large- and medium-vessel vasculitis, and Still’s disease, are seronegative, so diagnosis in early or atypical presentations requires longitudinal reassessment rather than point-in-time evaluation.

The harms of misdiagnosis are not benign. Patients labeled with autoimmune disease may face prolonged anxiety, repeated testing, unnecessary referrals, and exposure to glucocorticoids, hydroxychloroquine, biologics, and other immunosuppressants that carry metabolic, skeletal, ophthalmologic, infectious, and financial risks without addressing the true driver of symptoms. Life decisions around pregnancy, employment, insurance, and identity may be altered, disproportionately affecting women. At the systems level, indiscriminate testing and referrals increase costs without improving outcomes. Clear communication is essential to the solution: framing fibromyalgia as a disorder of pain processing rather than tissue damage helps validate symptoms and explain the lack of response to immunosuppression, while diagnostic restraint should be presented as probability-based rather than inattentive. The article closes with principles for generalists, including reserving screening labs for targeted indications, refusing to let isolated low-titer results drive labeling, diagnosing functional disorders with validated criteria rather than by exclusion, and favoring longitudinal clinical assessment over serial laboratory testing. A pan-positive review of systems, the author concludes, should prompt diagnostic restraint rather than escalation, because precision in diagnosis requires resisting serologic noise and returning to reasoning grounded in probability, pattern recognition, and humility.

Subject of Research: Diagnostic anchoring on positive autoimmune serologies and its role in misdiagnosis of systemic autoimmune rheumatic disease in polysymptomatic patients

Article Title: Diagnostic Anchoring on Positive Autoimmune Serologies in Polysymptomatic Patients

Article References: Chatterjee, S. (2026). Diagnostic Anchoring on Positive Autoimmune Serologies in Polysymptomatic Patients. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10727-6

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10727-6

Keywords: diagnostic anchoring, antinuclear antibody, autoimmune serologies, base-rate neglect, fibromyalgia, central sensitization, systemic lupus erythematosus, misdiagnosis, iatrogenic harm, overdiagnosis, rheumatology, low-value care