PREDICTORS OF PRESSURE ULCER DEVELOPMENT AMONG SENIORS DURING EMERGENCY TRANSFERS TO HOSPITAL EMERGENCY DEPARTMENT
Bibliographic record
Abstract
Abstract Emergency transfers of seniors in long-term care facilities (LTCF) aged >65 to hospital emergency departments (ED) are common and carry with them risks that can lead to less-than-optimal quality of care and quality of life. Pressure ulcers are one such risk. We used data from the Older Persons Transitions in Care (OPTIC; N=637) study, conducted in two Canadian provinces in 2011 and 2012, to assess potential predictors of pressure ulcer development between the time that a resident is transported to the ED until the time they return to their original nursing home. Step-wise binary logistic regression was employed to identify predictors of pressure ulcer development during the transition. Potential predictors included length of transition, inpatient status, demographic, health variables (including incontinence). Among the 335 residents for whom we were able to gather new pressure ulcer data, 56 (16.7%) were identified as having developed new skin wounds upon return to the LTCF. Transitions from ED admission to return to LTCF averaged 106.7 hours (sd=143.6) with a median of 50.0 hours. Length of transition and whether the resident spent time as an inpatient emerged as the only predictors: longer transition times and spending time as an inpatient predict development of bed sores. These results speak to the need for improved monitoring and treatment of skin wounds during emergency transitions of older adults from LTCF.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".