The First 100 Children Treated in a Newly Established Pediatric Vertigo Center
Bibliographic record
Abstract
BACKGROUND: Correctly diagnosing dizziness in children is essential for appropriate management; nevertheless, healthcare professionals face challenges due to children's limited ability to describe their symptoms and their cooperation during physical examination. The objective of this study is to describe the first 100 patients seen at a newly established pediatric vertigo center. METHODS: This is a retrospective review of a consecutive series of 100 patients seen at our pediatric vertigo clinic in a tertiary referral center from August 2019 until June 2022. Comprehensive clinical data were collected. The diagnoses were established by 2 pediatric otolaryngologists based on validated diagnostic criteria. Trends in diagnosis, investigation, and treatment of these patients were analyzed. RESULTS: A total of 100 children were included in the study. Vestibular migraine was the most common diagnosis (20%) followed by benign paroxysmal vertigo of childhood (14%). Eleven patients had combined pathologies. Fifteen out of 70 children (21%) had abnormal audiograms, 30 out of 48 children (62.5%) had abnormal vestibular testing, and 6 out of 31 (19%) patients had abnormal imaging. Fifty-one children received medical treatment, 23 received vestibular physiotherapy, and 9 patients had particle repositioning maneuvers; moreover, 17 of these patients received multimodal treatment. CONCLUSIONS: Our analysis suggests that imaging and audiology testing have relatively low yield in the assessment of pediatric vertigo. On the other hand, vestibular testing detected a high proportion of abnormalities, such as saccadic pursuit, vertical nystagmus, central positional nystagmus, and abnormal directional preponderance, particularly associated with vestibular migraine. Given the complexity of diagnosing vertigo in children, it is critical to establish multidisciplinary specialized centers capable of providing accurate diagnosis and treatment for these children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".