Clinical Assessment of Dizzy Patients: The Necessity and Role of Diagnostic Tests
Bibliographic record
Abstract
Over administration of diagnostic tests in health care settings is a critical issue, imposing a great deal of expenditure on health sector. Vertigo and dizziness are common complaints of many patients who seek medical advice, and the vast majority of them undergo several evaluations, including Brain Magnetic Resonance Imaging (MRI), Laboratory tests, Pure Tone Audiometry (PTA), and Electrocardiography (ECG). The aim of this study was to investigate the performing rate of these diagnostic tests, and to evaluate their necessity and medical indications. This study was conducted on 270 dizzy patients referred to Apadana Dizziness and Vertigo Clinic, Ahvaz, Iran, from July 2008 to February 2013. Of these, 71.9% were diagnosed with peripheral lesions while laboratory assessment (58.1%) and brain MRI (38.1%) were the most requested tests. Age was an important factor, affecting the frequency of performing the ECG and Brain MRI. Medications were still administered widely even to those who seemed to respond well enough to vestibular rehabilitation. These findings revealed that many unnecessary and time-consuming diagnostic tests were performed, which had minor contribution to the final diagnosis and treatment of the patients. Therefore, a modification in the assessment methods of the dizzy patients with emphasis on history and clinical presentation seems essential.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".