Reducing Hospitalisations With a Skin and Soft Tissue Infection Clinic
Bibliographic record
Abstract
RATIONALE: Patients with skin and soft tissue infection are often admitted to hospital despite compelling evidence that many can be managed safely as outpatients. This quality improvement study reports the outcomes of an outpatient skin and soft tissue infection programme implemented at an academic acute-care hospital in Toronto, Canada. METHODS: The intervention was an outpatient care pathway for patients with suspected skin and soft tissue infection who may otherwise have required admission to hospital. The programme was implemented within the existing general internal medicine outpatient clinic and primarily involved the addition of part-time advanced practice wound care nurses. The main outcome was the number of hospital inpatient days for skin and soft tissue infection. Data were analysed for 4 years pre-intervention (June 2016-May 2020) and 2 years post-intervention (June 2020-May 2022). Another acute-care hospital in the same network which did not undergo the intervention was included as a control. RESULTS: During the 2-year post-intervention period there were 465 clinic visits with the programme (mean of 19/month). The median number of inpatient days for skin and soft tissue infection decreased from 224 per month before the intervention to 148 per month after the intervention (a reduction of 34%). There was no reduction in inpatient days for skin and soft tissue infection at the control site or among all diagnoses at the intervention site. CONCLUSIONS: The implementation of an outpatient skin and soft tissue infection programme was associated with a sustained 34% reduction in inpatient days for skin and soft tissue infection. This study demonstrates the benefits of enhancing an existing outpatient internal medicine clinic through the creation of a streamlined care pathway and adding interdisciplinary expertise.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".