Bibliographic record
Abstract
One of the recent developments is to treat the disease very early in its development, exactly as is being done in asthma. The aim is to evaluate whether it is possible to modify the disease with long-term management. Can flare-ups be prevented? Is it possible to prevent the ‘atopic march’? The typical progress of a patient with atopic dermatitis (AD) was documented by Kissling & Wuthrich (1). They documented the development of eczema in 106 patients from the infantile phase, through childhood and adolescence into adulthood. The majority of patients experienced recurrent cycles of flare-ups, each of which was controlled by steroids. This recurrent cycle of events is distressing to parents who are also concerned about the ‘atopic march’ (2). Atopic eczema is in most cases the first manifestation of atopic disposition; eczema in childhood, compounded by food allergy, leading to asthma and long-term rhinitis. The general approach to treating AD is to treat relapses/flare-ups and when controlled to withdraw active treatment and use emollients (until the next flare-up). A recent trial has investigated whether continued application of a steroid can prevent or delay the cycle of relapses and treatment. Berth-Jones et al. (3) conducted a randomized vehicle (emollient) controlled trial in patients aged 12–65 with moderate to severe disease, to determine whether fluticasone propionate (at two strengths of 0.05% and 0.005%) can prevent relapses. There was a 1-month stabilization phase, followed by maintenance treatment of both ‘healed’ skin and new areas. The primary end point of the study was the time to relapse. A significant (pv0.001) prolongation of remission with application of fluticasone propionate 0.05% was seen. Significant benefit was also seen with fluticasone propionate 0.005%, but the benefit and statistical significance was reduced (p50.01). NEW THERAPEUTIC STATEGIES AND TARGETS
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".