Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the results from recent studies that assessed the burden of allergic diseases on the patients' every-day life, or contributed to new advances in monitoring quality-of-life in allergic disorders. This review will not report the numerous clinical trials that included quality-of-life as an outcome. RECENT FINDINGS: Quality-of-life impairment was investigated in patients with asthma, allergic rhinitis, atopic dermatitis or food allergy. A new questionnaire was validated for patients with yellow jacket allergy. At the same time, the properties of formerly developed questionnaires were further investigated and tools were developed to assess factors related to quality-of-life, such as work functioning, or perceived control of the disease. SUMMARY: Allergic disorders are associated with a variety of limitations in the patients' daily life. The relative burden of concomitant disorders, however, has not been thoroughly investigated. When monitoring quality-of-life, investigators should ensure that the results are not biased by confounding factors that may affect quality-of-life. Moreover, the uncertainty around estimates of change in health status related to treatment and around standard thresholds for clinical significance should be taken into account before drawing inference as regards the treatment worthiness.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".