Diagnostic Agreement in Subepidermal Blistering Diseases: Is <scp>ELISA</scp> Test Reliable as Direct Immunofluorescence? A Systematic Review, Meta‐Analysis, and Trial Sequential Analysis
Bibliographic record
Abstract
ABSTRACT Objectives Direct immunofluorescence ( DIF ) is the gold standard for diagnosing subepidermal blistering diseases ( SBDs ). However, DIF requires specialized expertise; therefore, alternative immunological methods such as enzyme‐linked immunosorbent assays ( ELISA ) are worth exploring. The aim of this review was to evaluate the diagnostic agreement between DIF and ELISA for the diagnosis of SBD . Materials and Methods A systematic literature review of international journals and electronic databases ( MEDLINE via OVID , Scopus, and Web of Science) was conducted between their inception and December 2023. The risk of bias and overall quality of evidence were assessed using the Newcastle‐Ottawa Scale and the GRADE system. Results Of the 1691 articles identified, 38 were included in the qualitative synthesis and 37 in the quantitative analysis. Three meta‐analyses were developed and revealed the superiority of DIF over ELISA , with the following risk differences: BP180 , 0.26 (95% CI : 0.19–0.33, p < 0.00001, I 2 : 96%); BP230 , 0.59 (95% CI : 0.47–0.71, p < 0.00001, I 2 : 98%); and Lam332, 0.82 (95% CI : 0.70–0.94, p < 0.00001, I 2 : 89%). Conclusions DIF remains the gold standard for the diagnosis of autoimmune SBDs . ELISA can serve as a complementary diagnostic tool, especially as a follow‐up instrument for patients with SBD , owing to its low invasiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".