The Care Triangle: Determining the Gaps in the Management of Atopic Dermatitis
Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis (AD) is a chronic, relapsing, intensely pruritic dermatosis that usually affects infants, children, and young adults. The treatment of AD entails an individualized regimen that depends on the age of the patient, the stage and variety of lesions present, the sites and extent of involvement, the presence of infection, and the previous response to treatment. OBJECTIVES: To identify the evidence surrounding potential strategies for closing these gaps-ultimately improving the quality of care, the care process itself, and patient outcomes-and to encourage discussions that help develop tools to bridge the gap between suggested therapy and what is done by the patient. METHODS: Review of the literature including searches on PubMed Central and Medline and in seminal dermatology texts. RESULTS: There are several disconnections between the evidence-based guidelines in the management of AD, what the individual dermatologist recommends, and what the patient does. CONCLUSION: Applying the concept of the care triangle requires a balance of evidence-based medicine, the physician's experiences and the patient's needs and expectations in the decisions surrounding appropriate management of the disease.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".