Still dizzy after all these years: a 90-year-old woman with a 54-year history of dizziness
Bibliographic record
Abstract
This report presents the case of a 90-year-old female with a 54-year history of dizziness, which has been exhaustively investigated. Over the years, the patient made 59 visits to her family doctor and 18 visits to various specialists, as well as emergency department visits and hospitalisations. In detailing the exhaustive investigations and referrals that the patient has undergone over many years (with inconclusive results), this case illustrates the myriad challenges in diagnosing and treating chronic dizziness in the older. The authors suggest that, in complex cases characterised by multimorbidity and polypharmacy, a function-oriented approach is indicated. In place of the conventional 'diagnose and treat' model, a functional approach to ongoing care emphasises the symptom management, improvement of function and quality of life. To optimise patient outcomes, an interprofessional team approach is preferred.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".