Developing evidence-based guidance for assessment of suspected infections in care home residents
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to update and refine an algorithm, originally developed in Canada, to assist care home staff to manage residents with suspected infection in the United Kingdom care home setting. The infections of interest were urinary tract infections, respiratory tract infections and skin and soft tissue infection. METHOD: We used a multi-faceted process involving a literature review, consensus meeting [nominal group technique involving general practitioners (GPs) and specialists in geriatric medicine and clinical microbiology], focus groups (care home staff and resident family members) and interviews (GPs), alongside continual iterative internal review and analysis within the research team. RESULTS: Six publications were identified in the literature which met inclusion criteria. These were used to update the algorithm which was presented to a consensus meeting (four participants all with a medical background) which discussed and agreed to inclusion of signs and symptoms, and the algorithm format. Focus groups and interview participants could see the value in the algorithm, and staff often reported that it reflected their usual practice. There were also interesting contrasts between evidence and usual practice informed by experience. Through continual iterative review and analysis, the final algorithm was finally presented in a format which described management of the three infections in terms of initial assessment of the resident, observation of the resident and action by the care home staff. CONCLUSIONS: This study has resulted in an updated algorithm targeting key infections in care home residents which should be considered for implementation into everyday practice.
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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 | 0.164 | 0.429 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.022 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.011 | 0.009 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".