Medical ID use by international patients with Aspirin-Exacerbated Respiratory Disease
Bibliographic record
Abstract
BACKGROUND: Patients widely use medical identification (ID) to indicate their food and drug allergies, and chronic medical conditions. One chronic condition for which patients are recommended to use a form of medical ID is Aspirin-Exacerbated Respiratory Disease (AERD), a disease characterized by the presence of asthma, chronic rhinosinusitis with nasal polyps and sensitivity to aspirin and other COX-1 inhibitors, including nonsteroidal anti-inflammatory drugs (NSAIDs). The uptake of medical ID use in AERD is unknown and has not been widely studied in this population. METHODS: We conducted a cross-sectional survey study to measure the perception of the need to use a medical ID and its use by patients with AERD internationally. RESULTS: 245 members of an online AERD support group completed an online survey. The majority (80%, n = 198) of the participants did not use any form of medical ID. The participants reported that the lack of knowledge and awareness about the importance of using a medical ID was the most common reason for not using it. CONCLUSION: This international survey found that the majority of the AERD patient respondents did not use a medical ID. The most common reasons for nonuse were not knowing that it is recommended for their condition and that the patients did not consider it necessary. The results highlight the need for further patient and health care provider education.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".