Bibliographic record
Abstract
Pneumonia and other lower respiratory infections occur frequently among older adults residing in long-term care facilities. These infections are the most common reason for why residents are transferred to hospital. Such transfer is not only a frightening experience for these seniors, but may also be associated with multiple risks including decline in functional status, falls, delirium, and acquisition of multi-drug resistant bacteria. A clinical pathway or algorithm designed to manage residents with pneumonia on-site in the nursing home may reduce hospitalization and associated hazards. This mixed model research project used both quantitative and qualitative research methods to determine the safety, effectiveness, and experience of using a clinical pathway to manage nursing-home acquired pneumonia in a randomized controlled trial. We found that pneumonia severity was a strong predictor of hospitalization and mortality, indicating that clinicians currently do a good job identifying and hospitalizing those residents with a high risk of mortality. However, use of a pneumonia clinical pathway reduced the rate of hospitalization of nursing home residents by a weighted mean of 12% (p = 0.001), corresponding to an average cost saving of $1,016 per resident managed, with no effect on clinical outcomes. These data indicate that for many residents with lower respiratory infections, hospitalization does not improve clinical outcomes. Therefore, implementing this simple and safe clinical pathway in Canada could translate to a savings for the Canadian healthcare system of $70 million annually. Our qualitative research findings support the use of the clinical pathway, demonstrating that it is in accordance with resident and family member preferences and is considered desirable and feasible by nursing home staff and management. The use of a mixed model approach provides policy-relevant data including clinical effectiveness, resident and family member preferences, and insight into sustainability from the perspective of healthcare providers and clinical administrators.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".