Relationship between dizziness and learning difficulties in schoolchildren: an integrative review
Bibliographic record
Abstract
ABSTRACT Purpose: this study aims to verify the level of scientific evidence on the relationship between dizziness and academic achievement in childhood. Methods: the study was performed using the following search terms: dizziness, vertigo, child, learning, spelling, learning skills, academic skills, reading, and their correspondents in Portuguese in the following databases: PubMed, Scielo, LILACS and PsycINFO. Observational studies that examined the relationship between dizziness and academic achievement in childhood, published between 2007 and 2017, were included. Articles that did not allow access to the full text, and studies based on samples with motor, hearing, and cognitive disorders were excluded. Results: we initially found 315 articles and three met the established inclusion and exclusion criteria. These were scored according to the Newcastle-Ottawa Modified Scale with scores between 2 and 3 and as IIb according to the American Speech-Language Hearing Association levels of evidence and quality indicators. Conclusion: the results of this integrative review showed a low level of scientific evidence on the relationship between dizziness and academic achievement in childhood. It is important to emphasize the importance of improving study design to better understand their relationship, to allow provision of the best preventive, assessment, and intervention methods.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".