Olfactory Dysfunction in Children: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: Olfactory disorders are well-studied in the adult population, however, there is a paucity of literature characterizing olfactory dysfunction in pediatric patients. The purpose of this scoping review was to identify known causes of olfactory loss in pediatric populations, clarify the extent of use and validity of smell tests, and summarize current therapies for olfactory loss. DATA SOURCES: PubMed, Ovid MEDLINE, and Web of Science. REVIEW METHODS: Databases were systematically searched in September 2020. Two independent reviewers conducted the title and abstract screen, followed by review of full-texts for inclusion based on preset inclusion and exclusion criteria. Extracted data included study type, age/age-range of participants, gender, radiological evidence of olfactory dysfunction, types and results of smell tests used, etiology of olfactory loss, and therapies employed for olfactory loss. RESULTS: A total of 103 articles (n = 1654) were eligible for final data extraction. The University of Pennsylvania Smell Identification Test was used most frequently for smell testing (21% of studies). In total, 45 causes of olfactory dysfunction have been elucidated by this study: 22 congenital and 23 acquired. Few therapies were described, and all were specific to the etiology of olfactory loss. CONCLUSION: Olfactory dysfunction has a wide range of etiologies in the pediatric population, and clinicians should have a diagnostic algorithm for how to identify a cause should they encounter it in practice. If no etiology can be identified, education around safety should be provided to both the patient and their caregivers.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".