A retrospective analysis of two tertiary care dizziness clinics: A multidisciplinary chronic dizziness clinic and an acute dizziness clinic
Bibliographic record
Abstract
BACKGROUND: Vertigo remains a diagnostic challenge for primary care, emergency, and specialist physicians. Multidisciplinary clinics are increasingly being employed to diagnose and manage patients with dizziness. We describe, for the first time in Canada, the clinical characteristics of patients presenting with chronic and acute dizziness to both a multidisciplinary chronic dizziness clinic (MDC) and a rapid access dizziness (RAD) clinic at The Ottawa Hospital (TOH). METHODS: We performed a retrospective review of all patients presenting to the MDC and RAD clinics at TOH from July 2015 to August 2017. RESULTS: Overall, 211 patients (median age: 61 years old) presented to the RAD clinic and 292 patients (median age: 55 years old) presented to the MDC. In the RAD clinic, 63% of patients had peripheral dizziness, of which 55% had BPPV, and only one patient had functional dizziness. Interestingly, only 25% of RAD diagnoses were concordant with emergency department diagnoses; moreover, only 33% of RAD patients had HiNTS completed, while 44% had CT scans, of which only one scan had an abnormal finding. Prior to assessment, all patients in the MDC had an unclear cause of dizziness. 28% of patients had vestibular dizziness and 21% had functional dizziness, of which 43% had persistent postural perceptual dizziness. Moreover, 12% of patients with functional dizziness also suffered from comorbid severe anxiety and depression. CONCLUSIONS: Dizziness is a heterogeneous disorder that necessitates multidisciplinary care, and clinics targeting both the acute and chronic setting can improve diagnostic accuracy, ensure appropriate diagnostic testing, and facilitate effective care plans for patients with dizziness.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".