Longitudinal Patient Outcomes in Chronic Dizziness: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Chronic dizziness can cause significant functional impairment. Outcome measures used in this patient population have not been examined systematically. Consequently, providers lack consensus on the ideal outcome measures to assess the impact of their interventions. OBJECTIVE AND METHODS: We conducted a scoping review to summarize existing literature on outcomes in chronic dizziness (with a minimum of 6 mo of patient follow-up). Among other details, we extracted and analyzed patient demographics, medical condition(s), and the specific outcome measures of each study. RESULTS: Of 19,426 articles meeting the original search terms, 416 met final exclusion after title/abstract and full-text review. Most studies focused on Ménière's disease (75%) and recurrent benign paroxysmal positional vertigo (21%). The most common outcome measures were hearing (62%) and number of attacks by American Academy of Otolaryngology-Head & Neck Surgery criteria (60%). A minority (35%) looked formally at quality-of-life metrics (Dizziness Handicap Index or other). CONCLUSIONS: Ménière's disease and benign paroxysmal positional vertigo are overrepresented in literature on outcome assessment in chronic dizziness. Objective clinical measures are used more frequently than quality-of-life metrics. Future work is needed to identify the optimal outcome measures that reflect new knowledge about the most common causes of chronic dizziness (including persistent postural-perceptual dizziness and vestibular migraine) and consider what is most important to patients.
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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 | 0.016 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 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".