Impact and Associations of Atopic Dermatitis Out-of-Pocket Health Care Expenses in the United States
Bibliographic record
Abstract
Department of Community Medicine, Muzaffarnagar Medical College, Muzaffarnagar, India [email protected] Department of Kaya Chikitsa, Sham-e-Ghausia Minority Ayurved Medical College and Hospital, Ghazipur, India. ORCID: https://orcid.org/0000-0002-4046-5035 ORCID: https://orcid.org/0000-0003-2699-4771 The authors have no funding or conflicts of interest to declare. D.S. and P.J. have contributed equally to this work. D.S. states that all authors had full access to the full data in the study and accepts the responsibility to submit for publication. Both authors have substantial contributions to the conception or design of the work or the acquisition, analysis, or interpretation of data; drafted the work or revised it critically for important intellectual content; approved the final version of the manuscript; and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. There has been no data collection of any human or animal subject (or participant) in this study. The data that support the findings of this study are available on the website linked to the digital object identifier of references used in the manuscript. These data are available in the public domain.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".