Dizziness at a Canadian tertiary care hospital: A cost-of-illness study
Bibliographic record
Abstract
BACKGROUND: In the Canadian health care system, determining overall costs associated with a particular diagnostic subgroup of patients, in this case dizzy patients, is the first step in the process of determining where costs could be saved without compromising patient care. This study is the first Canadian study that evaluates these costs at a tertiary care hospital and will allow for the extrapolation of cost data for other similar academic health science centers, regional health initiatives, and provincial healthcare planning structures. METHODS: 2014 with a main diagnosis of dizziness or dizziness-related disease. De-identified patient information was acquired through TOH Data Warehouse and included a patient's sex, age, arrival and departure dates, Elixhauser co-morbidity score, location of presentation (emergency department or admitted inpatient) presenting complaint, final diagnosis code, any procedure codes linked to their care, and the direct and indirect hospital costs linked with any admission. We derived the mean hospital costs and 95% confidence interval for each diagnosis. We obtained the number of patients who were diagnosed with dizziness within Ontario in year 2015-16 from Canadian Institute for Health Information (CIHI). A simple frequency multiplication was performed to estimate the total cost burden for Ontario based on the cost estimate for the same year obtained from TOH. Cost data were presented in 2017 Canadian dollars. RESULTS: The average total hospital cost per patient with dizziness for the entire cohort is $450 (SD = $1334), with ED only patients costing $359 (SD = $214). The total estimated hospital cost burden of dizziness in Ontario is $31,202,000 (95% CI $29,559,000 - 32,844,000). CONCLUSIONS: The estimated annual costs of emergency department ambulatory and inpatient dizziness in Ontario was calculated to be approximately 31 million dollars per year. This is the first step in identifying potential areas for cost savings to aid local and provincial policy-makers in allocation of health care spending.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".