Systematic review of atopic dermatitis disease definition in studies using routinely collected health data
Bibliographic record
Abstract
Atopic dermatitis (AD) is a skin disease that involves dry, itchy skin and scaly rashes. It is a common disease, especially in children, and is often simply referred to as ‘eczema’. In recent years, researchers have used medical records stored electronically and information from insurance claims to study AD. Because these databases are designed for clinical care and billing purposes and not explicitly for research, correctly identifying patients with AD in these data can be challenging. If patients are not correctly identified, studies using these databases may find misleading results. This study, conducted by a group of researchers from the United States, Canada, and United Kingdom, sought to find out how researchers identify patients with AD in data routinely collected by hospitals and health organizations. We systematically searched three large databases (PubMed, EMBASE, and Web of Science) and identified 59 studies which used routinely collected data to find out how common AD is in different populations. We found that researchers identified patients with AD in a variety of different ways. For example, patients’ records are often given numerical codes to represent medical diagnoses, and researchers used different sets of codes. Researchers also varied in whether they used information about patients’ medications and number of hospital or clinic visits related to AD. In addition, researchers rarely showed that their approach to identifying AD patients was accurate. Results from studies of AD will be difficult to interpret and compare if researchers do not have reliable ways of identifying AD patients in electronic medical records and insurance claims. Going forward, more studies are needed to develop and test strategies for identifying AD patients in these types of data so that the approaches used by researchers will be standardized. The authors offer suggestions for how to describe the approaches so that they will be more comparable.
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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 | 0.019 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".