Bibliographic record
Abstract
Rehabilitation patients are at increased risk of falling due to multiple risk factors, while being in a unique environment that encourages increased independence and functional gains. Evidence Falls account for approximately 90 % of all fractures in people age 65 years and older and are the 6th leading cause of Sentinel Events according to The Joint Commission. Strategy To help prevent these adverse events that hinder rehabilitation patients from working toward improving their functional outcomes, a Fall Prevention Interdisciplinary Team was formed in 2004. A comprehensive education campaign was implemented. The fall prevention program encompasses a transdisciplinary and multi-directional approach, where every employee in the facility has been empowered to promote patient safety to reduce the number of falls. Practice Change Identification of at risk patients was our first challenge. Once high risk patients were identified, a well designed evidence-based protocol was implemented. Shortly after implementing fall reduction strategies, the facility became restraint-free, which required further revisions to our processes. Evaluation Monthly interdisciplinary team meetings are conducted to evaluate each patient fall, including root cause(s) of the fall, medications, etc. The team trends fall prevalence, injuries related to falls, time of fall, shift of fall, day of the week, etc. Results As a result of this program, the fall prevalence rate was reduced from 11.5 in 2005 to 7.67 in 2008, a decrease of 33%, without the use of restraints. The prevalence rate for 2nd quarter of fiscal year 2009 is 5.66.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.165 | 0.043 |
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".