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Record W1969696304 · doi:10.1080/14038190310016625

What happens with the dizzy patient in primary health care? Does education influence treatment?

2004· article· en· W1969696304 on OpenAlexaff
Eva Ekvall-Hansson, Nils-Ove Månsson, Anders Håkansson

Bibliographic record

VenueAdvances in Physiotherapy · 2004
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsCentre for Family Medicine
Fundersnot available
KeywordsMedicineVertigoPhysical therapyRehabilitationIntervention (counseling)Vestibular rehabilitationMedical recordPhysical medicine and rehabilitationHealth careNursingSurgery

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.290
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2004
Admission routes1
Has abstractyes

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