Proprioceptive Cervicogenic Dizziness Care Trajectories in Patient Subpopulations: A Scoping Review
Bibliographic record
Abstract
Proprioceptive cervicogenic dizziness (PCGD) is the most prevalent subcategory of cervicogenic dizziness. There is considerable confusion regarding this clinical syndrome's differential diagnosis, evaluation, and treatment strategy. Our objectives were to conduct a systematic search to map out characteristics of the literature and of potential subpopulations of PCGD, and to classify accordingly the knowledge contained in the literature regarding interventions, outcomes and diagnosis. A Joanna Briggs Institute methodology-informed scoping review of the French, English, Spanish, Portuguese and Italian literature from January 2000 to June 2021 was undertaken on PsycInfo, Medline (Ovid), Embase (Ovid), All EBM Reviews (Ovid), CINAHL (Ebsco), Web of Science and Scopus databases. All pertinent randomized control trials, case studies, literature reviews, meta-analyses, and observational studies were retrieved. Evidence-charting methods were executed by two independent researchers at each stage of the scoping review. The search yielded 156 articles. Based on the potential etiology of the clinical syndrome, the analysis identified four main subpopulations of PCGD: chronic cervicalgia, traumatic, degenerative cervical disease, and occupational. The three most commonly occurring differential diagnosis categories are central causes, benign paroxysmal positional vertigo and otologic pathologies. The four most cited measures of change were the dizziness handicap inventory, visual analog scale for neck pain, cervical range of motion, and posturography. Across subpopulations, exercise therapy and manual therapy are the most commonly encountered interventions in the literature. PCGD patients have heterogeneous etiologies which can impact their care trajectory. Adapted care trajectories should be used for the different subpopulations by optimizing differential diagnosis, treatment, and evaluation of outcomes.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.017 |
| 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) | 0.003 | 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".