The Frequency and Utility of Drug Cessation Trials in Older Adults with Chronic Eczematous Dermatitis of Unknown Etiology: A Retrospective Cohort Study
Bibliographic record
Abstract
Abstract: Background: Eczematous dermatitis is a major cause of recalcitrant pruritic eruptions in older adults. Although some medications have been implicated, there are limited data demonstrating the utility of medication changes. Objective: To investigate the utility and possible harms of drug cessation trials (DCTs) in chronic eczematous eruptions in the aging (CEEA). Methods: This is a retrospective cohort study utilizing electronic health records of DCTs in adults older than 65 years with CEEA. Results: We identified 646 patients >65 years with new onset eczematous eruptions, 89 (14%) of whom had no identifiable etiology. In this cohort, 35 patients underwent a total of 40 DCTs. Although there was mention of improvement in 17.5% (7/40), all patients sought tertiary care for their persistent rash. Negative outcomes occurred in 45% (18/40), all of which were due to exacerbation of a comorbidity that the medication was prescribed to treat. Conclusion: Our experience suggests that patients with CEEA undergo DCTs that do not improve their dermatitis and can lead to dangerous worsening of underlying conditions. Further study of the etiology of CEEA is needed.
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.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".