COVID‐19 pandemic: Practical considerations on the organization of an allergy clinic—An EAACI/ARIA Position Paper
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) has evolved into a pandemic infectious disease transmitted by the severe acute respiratory syndrome coronavirus (SARS-CoV-2). Allergists and other healthcare providers (HCPs) in the field of allergies and associated airway diseases are on the front line, taking care of patients potentially infected with SARS-CoV-2. Hence, strategies and practices to minimize risks of infection for both HCPs and treated patients have to be developed and followed by allergy clinics. METHOD: The scientific information on COVID-19 was analysed by a literature search in MEDLINE, PubMed, the National and International Guidelines from the European Academy of Allergy and Clinical Immunology (EAACI), the Cochrane Library, and the internet. RESULTS: Based on the diagnostic and treatment standards developed by EAACI, on international information regarding COVID-19, on guidelines of the World Health Organization (WHO) and other international organizations, and on previous experience, a panel of experts including clinicians, psychologists, IT experts, and basic scientists along with EAACI and the "Allergic Rhinitis and its Impact on Asthma (ARIA)" initiative have developed recommendations for the optimal management of allergy clinics during the current COVID-19 pandemic. These recommendations are grouped into nine sections on different relevant aspects for the care of patients with allergies. CONCLUSIONS: This international Position Paper provides recommendations on operational plans and procedures to maintain high standards in the daily clinical care of allergic patients while ensuring the necessary safety measures in the current COVID-19 pandemic.
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.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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 it