Bibliographic record
Abstract
India is the home of nearly 20% of the global population with 1.35 billion people. Of all non-communicable diseases, allergic diseases such as allergic rhinitis (AR) and asthma appear to have increased in India over the past decades. Approximately 22% of adolescents currently suffer from AR in India. However, owing to the lack of adequate epidemiological studies in India, particularly in rural and suburban areas, this number may misrepresent the true burden of this disease. While the risk factors for AR are mainly environmental exposures or genetic factors, several new environmental, social, and behavioural risk factors such as the presence of dumpsters near residences, movement of vehicles near homes, and exposure to artificial light at night have been found to be associated with AR. However, despite international guidelines, the diagnosis and management of AR in India are often suboptimal, for multiple reasons such as the lack of specialized training in allergy and immunology among Indian clinicians, the lack of diagnostic facilities, and the high cost of medications. This review aims at highlighting the current scenario of AR in India and how it differs from the rest of the world. It also highlights the need for developing a strategic approach to enhance the quality of care for allergic diseases by upgrading education and training for healthcare professionals, creating awareness among clinicians and patients, and involving stakeholders and policymakers in making treatments accessible and affordable to patients.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".