A multidisciplinary assessment instrument to predict fall risk in hospitalized patients: A prospective matched pair case study
Bibliographic record
Abstract
Objective: To identify fall risk for hospitalized patients utilizing a multidisciplinary assessment tool based on patients’ physical, mental, pharmacological, and metabolic data. Methods: A prospective case-control design comparing 48 patients who fell (incidence group) and 48 patients who did not experience fall (control group), based on patients’ age, gender, and hospital unit location. The study was conducted over an 8-month period at a large academic hospital. Setting: The Methodist Hospital, tertiary care academic referral center with 824 operating beds in Houston, TX. Participants: One hundred and twenty patients, sixty patients who fell, and sixty control subjects. Main Outcome Measures: The sensitivity and specificity of variables identified in logistic regression are able to distinguish patients who fell from patients who did not fall. Results: Logistic regression results identified six variables (2 summary variables and 4 individual variables) that correctly classified patients with 90% sensitivity (patients who fell) and 90% specificity (patients who did not fall). The first variable was an 11-item summary variable that included history, weakness or balance problem, altered mental status or confusion, visual impairment, dizziness or vertigo, urinary tract infection or abnormal urinary analysis (UA), diuretics/IV drips, continence, acute renal failure (ARF), antihypertensives and narcotics. The second variable represented the combination of 3 medication classes: neuroleptic, anticonvulsant and antidepressant. The third variable that had a negative impact on fall risk was the presence of a therapeutic anticoagulant. The other 3 significant variables were hypoglycemia, vital sign abnormality, and low hemoglobin. Conclusions: A multidisciplinary fall-risk assessment tool that screens combinations of physiological, pharmacological and metabolic patient factors improves the probability of correctly distinguishing patients who were more likely to fall from those patients who were less likely to fall.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".