The 2020 International Alliance for the Control of Scabies Consensus Criteria for the Diagnosis of Scabies
Bibliographic record
Abstract
BACKGROUND: Scabies is a common parasitic skin condition that causes considerable morbidity globally. Clinical and epidemiological research for scabies has been limited by a lack of standardization of diagnostic methods. OBJECTIVES: To develop consensus criteria for the diagnosis of common scabies that could be implemented in a variety of settings. METHODS: Consensus diagnostic criteria were developed through a Delphi study with international experts. Detailed recommendations were collected from the expert panel to define the criteria features and guide their implementation. These comments were then combined with a comprehensive review of the available literature and the opinion of an expanded group of international experts to develop detailed, evidence-based definitions and diagnostic methods. RESULTS: The 2020 International Alliance for the Control of Scabies (IACS) Consensus Criteria for the Diagnosis of Scabies include three levels of diagnostic certainty and eight subcategories. Confirmed scabies (level A) requires direct visualization of the mite or its products. Clinical scabies (level B) and suspected scabies (level C) rely on clinical assessment of signs and symptoms. Evidence-based, consensus methods for microscopy, visualization and clinical symptoms and signs were developed, along with a media library. CONCLUSIONS: The 2020 IACS Criteria represent a pragmatic yet robust set of diagnostic features and methods. The criteria may be implemented in a range of research, public health and clinical settings by selecting the appropriate diagnostic levels and subcategories. These criteria may provide greater consistency and standardization for scabies diagnosis. Validation studies, development of training materials and development of survey methods are now required. What is already known about this topic? The diagnosis of scabies is limited by the lack of accurate, objective tests. Microscopy of skin scrapings can confirm the diagnosis, but it is insensitive, invasive and often impractical. Diagnosis usually relies on clinical assessment, although visualization using dermoscopy is becoming increasingly common. These diagnostic methods have not been standardized, hampering the interpretation of findings from clinical research and epidemiological surveys, and the development of scabies control strategies. What does this study add? International consensus diagnostic criteria for common scabies were developed through a Delphi study with global experts. The 2020 International Alliance for the Control of Scabies (IACS) Criteria categorize diagnosis at three levels of diagnostic certainty (confirmed, clinical and suspected scabies) and eight subcategories, and can be adapted to a range of research and public health settings. Detailed definitions and figures are included to aid training and implementation. The 2020 IACS Criteria may facilitate the standardization of scabies diagnosis.
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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 | 0.135 | 0.183 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".