Nursing Home Residents’ Use of Radiography in New Brunswick: A Case for Mobile Radiography?
Bibliographic record
Abstract
Introduction: Identifying ways to eliminate unnecessary transfer of nursing home (NH) residents to hospitals provides an opportunity to improve outcomes and use scarce healthcare resources more efficiently. This study's goal was to better understand where NH residents access X-ray (XR) and computed tomography (CT) scans and to determine if there was a case for mobile radiography policies in New Brunswick. Methods: A retrospective analysis of all the visits to the emergency department (ED) and outpatient imaging departments in two hospitals in Saint John, New Brunswick, in 2020, that involved XR or CT investigations was conducted. Results: There were 521 visits by 311 unique NH residents and 920 investigations (688 XR and 232 CT scans). Most investigations were ordered in the ED (696 of 920; 75.6%; confidence interval: 72.8-78.3%). Of the NH residents who visited the ED and received either an XR or a CT scan, 33.2% received only XR imaging and were discharged back to the NH after a mean ED stay of 5.15 hours. Discussion: The pattern of NH residents' use of the ED for their imaging needs supports the creation of mobile XR policies to deliver more safe and efficient care in a Canadian medium population urban centre.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".