Evaluation of Paraclinical Tests in the Diagnosis of Cervicogenic Dizziness
Bibliographic record
Abstract
OBJECTIVE: To assess the utility of the paraclinical tests in patients presenting with clinical diagnosis of cervicogenic dizziness. STUDY DESIGN: Case controlled. SETTING: Otolaryngology clinic of a tertiary referral hospital center. PATIENTS: Twenty-five subjects with cervicogenic dizziness and 25 subjects with benign paroxysmal positional vertigo. MAIN OUTCOME MEASURES: Symptoms description, Dizziness Handicap Inventory-short form (DHI), Trait anxiety score, cervical joint position error, the smooth pursuit neck torsion and cervical torsion tests on videonystagmography, and standing balance test (timed 10-meter walk with head turns). RESULTS: The results showed differences in reported symptoms, in mean cervical joint position error (p = 0.001), and cervical torsion test (p = 0.001) between the two groups. There was no between-group difference for DHI scores (p = 0.137), trait anxiety scores (p = 0.240), and walking test: time (p = 0.797), steps (p = 0.963). The Youden index is 0.60 for the predictive value of the cervical joint position error, and the smooth pursuit and the cervical torsion tests. CONCLUSION: This study showed differences in sensorimotor disturbances between the two groups, particularly in the control of head and eye movements and cervical proprioception. Patients with cervicogenic dizziness were more likely to (1) have a sensation of drunkenness and lightheadedness, (2) have pain induced during the physical examination of the upper cervical vertebrae, (3) have an elevated joint position error of 4.5 degrees during the cervical relocation test, and (4) exhibit more than 2 degrees per second nystagmus during the cervical rotation test. The walking test was not able to differentiate the two groups.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".