Economic Analysis of the Use of Anti-DFS70 Antibody Test in Patients with Undifferentiated Systemic Autoimmune Disease Symptoms
Bibliographic record
Abstract
OBJECTIVE: In patients with antinuclear antibodies (ANA) and undifferentiated features of systemic autoimmune disease, the coexistence of monospecific anti-dense fine speckled 70 (anti-DFS70) antibodies is associated with a lower risk of progression to overt disease. Therefore, they might help in correctly classifying ANA- positive patients and avoiding unnecessary followup diagnostic procedures. The aim of this study was to analyze the economic effect of the introduction of the anti-DFS70 antibody test in a hospital setting. METHODS: A case-control study was performed to detect monospecific anti-DFS70 antibodies in ANA-positive subjects with undifferentiated features (cases, n = 124) and with a defined systemic autoimmune disease (controls, n = 290). Based on current clinical practice, a decision tree was developed to represent the disease course of patients with undifferentiated features in the subsequent 3 years. A budget impact analysis (BIA) was performed to estimate the effect of implementing the screening for anti-DFS70 antibodies in the case group on the total costs. A sensitivity analysis was conducted to calculate the effect of the uncertainty of the input variables on the results. RESULTS: Among the 124 patients in the case group, 5 (4.0%) tested positive for anti-DFS70 antibodies versus 4/290 (1.4%) in the control group (p = not significant). The mean cost per patient under the current clinical practice decreased from €3274 to €3192 in our scenario. The BIA reports cost savings of €10,128. CONCLUSION: The introduction of anti-DFS70 antibody test would avoid unnecessary followup diagnostic procedures and minimize the use of health resources generated by suspicion of a potential systemic autoimmune 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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".