Helping pharmacists to reduce fall risk in long-term care
Bibliographic record
Abstract
BACKGROUND: One-third to one-half of adults older than 65 fall at least once per year. Fall prevention through medication management requires little effort and has consistently been shown to reduce risk of falls. The objective of this study was to further develop and perform preliminary pilot testing of an algorithm designed to assist consultant pharmacists in systematically identifying medications that might be modifiable, in order to reduce the risk of falls in older adults. We hypothesized that algorithm use would increase the number of fall-related medication change recommendations made to physicians. METHODS: Four consultant pharmacists were trained to use the algorithm during their routine medication reviews over a 3-week period. An informal survey was administered at the end of the study period to assess the algorithm. RESULTS: Overall, 51% of residents of long-term facilities had 1 or more recommendations for medication changes related to reducing fall risk (range 0-3 recommendations per resident), with an average 0.675 recommendations made per resident. There were more recommendations for men compared with women and for residents receiving more medications, but the number of recommendations did not correspond with age. All 4 pharmacists agreed that the algorithm was useful and worthwhile. DISCUSSION: The absolute 20% increase in recommendations related to falls supports the study hypothesis. Time was cited as a barrier to using the algorithm, but this should decrease with continued use of this tool. CONCLUSION: This preliminary study furthered the development of and confirmed the possible utility and acceptability of a fall risk-reducing algorithm that may be used in practice.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".