The use of the Morse Fall Scale in an acute care hospital
Bibliographic record
Abstract
Background: Patient falls in hospitals account for a high proportion of adverse events. Assessing patient risk is a vital part of a fall prevention program. When a fall risk assessment tool is used, it is imperative to use one which is suitable for the hospital. Objective: The purpose of this study was to test the predictive validity of the Morse Fall Scale (MFS) by assessing the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) on medicine units in an acute care hospital. Methods: Patient MFS scores were obtained from the medicine units. A total of 500 patient scores were collected along with a number of falls which occurred within 7 days of the fall risk assessment. Data were collected from November 2014 to March 2015. The setting was a large teaching hospital located in Ontario, Canada. Results: Using a cut-off point of 25 on the MFS, the sensitivity was 98% and the specificity was 8%. The PPV was 10% and the NPV was 97%. An MFS cut-off point of 55 provided the most balanced measure of sensitivity (87%) and specificity (34%) for accurate identification of fall risk. Conclusions: Findings suggest a change in practice is warranted as the values showed a poor balance between the sensitivity and specificity range. Recommendations for changes in practice include: changing the screening tool cut-off point from 25 to 55, or removing the use of a screening tool and assessing the risk by using another method.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".