Autoimmune pemphigus: diagnostic challenges, interdisciplinary interaction, and therapeutic errors
Bibliographic record
Abstract
Patient safety is a fundamental principle in the provision of medical care. In the Russian Federation, continuous efforts are undertaken to reduce the number of adverse events, including through systematic analysis of medical errors and undesirable incidents. To illustrate these issues, the article presents four clinical cases of patients with various forms of acantholytic pemphigus. Particular attention is given to the young age of the patients and the clinical course of specific forms of acantholytic pemphigus. The paper discusses shortcomings in medical care, including violations of differential diagnostic algorithms, lack of interdisciplinary continuity, and therapeutic strategy failures. It is reported that diagnostic and therapeutic errors contribute to a more severe course of dermatosis and the transformation of one pemphigus form into another. Timely diagnosis of pemphigus, which belongs to the group of autoimmune bullous dermatoses, is a crucial step in patient management and prevents progression of autoimmune forms in the absence of pathogenetic therapy. Diagnostic criteria include medical history, clinical presentation, cytological examination for acantholytic cells from the base of mucosal and/or skin erosions, histopathological examination of skin biopsy samples from lesions (including direct immunofluorescence methods), and/or indirect immunofluorescence testing of serum to detect IgG and IgA autoantibodies. The article emphasizes the importance of adherence to clinical guidelines, increasing awareness among physicians of related specialties about current features of bullous dermatoses, and improving coordination among specialists from different disciplines. Addressing these issues will improve the quality of medical care and enhance patient safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".