What happens with the dizzy patient in primary health care? Does education influence treatment?
Bibliographic record
Abstract
Dizziness is a common symptom, and recent research shows that physical activity and specific treatment of vertigo and dizziness are effective. The management of dizzy patients requires assessment and, when appropriate, treatment by a physiotherapist. We have therefore studied how general practitioners (GPs) handle patients with vertigo and dizziness, to find out whether treatment follows current research, emphasizing the importance of physical activity and vestibular rehabilitation. We also wanted to find out whether information and education concerning the importance of physiotherapy and rehabilitation had any influence on the choice of treatment. Searches were performed in medical records at two health care centres on two occasions, in 1998 and 2000. In 1999, an intervention in the form of education was given to the staff. Records from the 311 patients with dizziness/vertigo identified in the searches were read and measures taken by the GPs were registered. The most common procedures – blood tests, control of blood pressure and ECG – were more common in 2000 than in 1998. No patients were left without any measure in 2000, which was the case in 1998. Only a few patients were referred to physiotherapy (8% in 1998 and 12% in 2000). It seems that the intervention did not affect the ratio of patients referred to physiotherapy.
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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 | 0.005 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".