Cost-Analysis Of A Geriatrician-Led Falls Prevention Clinic Among Older Adults At High Risk Of Future Falls
Bibliographic record
Abstract
Abstract A geriatrician-led Falls Prevention Clinic is a best practice and evidence-based approach for falls prevention that has demonstrated feasibility and acceptability. The economic impacts of this approach, which directly impact its sustainability within the healthcare system, remain unknown. Our primary objective was to determine the costs and potential savings associated with a multi-disciplinary geriatrician-led Falls Prevention Clinic among older adults at high risk of falls. Operating costs of the Falls Prevention Clinic were determined based on operating costs (i.e., personnel, equipment, overhead), medical services plan costs, and personnel costs. Falls Prevention Clinic usage over 12 months was detailed based on monthly frequencies of new and repeat visits. Longitudinal health resource utilization was ascertained over 12 months. Cost savings from falls averted over 12 months were estimated. Main outcome measures included: operating costs of the clinic, estimated annual health resource utilization savings, number of falls over 12 months and health care costs (i.e., health care practitioner, hospital admissions, laboratory tests/investigations). A total of 543 patients were seen over a year, with 240 new and 303 follow-up patients. The total direct health resource utilization costs were 4,892 (7,767) (2024 CAD$) per person over 12 months. The annual estimated cost-saving of the clinic from fall prevention is 956,288 (2024 CAD$). The Fall Prevention Clinic provides a multi-disciplinary approach that is best practice, evidence-based for fall prevention. This approach has demonstrated feasibility and effectiveness and saves health care dollars; thus, it is an effective and economically attractive approach to consider for implementation.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".