Examination of hospital length of stay in Canada among patients with acute bacterial skin and skin structure infection caused by methicillin-resistant Staphylococcus aureus
Bibliographic record
Abstract
PURPOSE: Skin infections, particularly those caused by resistant pathogens, represent a clinical burden. Hospitalization associated with acute bacterial skin and skin structure infections (ABSSSI) caused by methicillin-resistant Staphylococcus aureus (MRSA) is a major contributor to the economic burden of the disease. This study was conducted to provide current, real-world data on hospitalization patterns for patients with ABSSSI caused by MRSA across multiple geographic regions in Canada. PATIENTS AND METHODS: This retrospective cohort study evaluated length of stay (LOS) for hospitalized patients with ABSSSI due to MRSA diagnosis across four Canadian geographic regions using the Discharge Abstract Database. Patients with ICD-10-CA diagnosis consistent with ABSSSI caused by MRSA between January 2008 and December 2014 were selected and assigned a primary or secondary diagnosis based on a prespecified ICD-10-CA code algorithm. RESULTS: Among 6,719 patients, 3,273 (48.7%) and 3,446 (51.3%) had a primary and secondary diagnosis, respectively. Among patients with a primary or secondary diagnosis, the cellulitis/erysipelas subtype was most common. The majority of patients presented with 0 or 1 comorbid condition; the most common comorbidity was diabetes. The mean LOS over the study period varied by geographic region and year; in 2014 (the most recent year analyzed), LOS ranged from 7.7 days in Ontario to 13.4 days in the Canadian Prairie for a primary diagnosis and from 18.2 days in Ontario to 25.2 days in Atlantic Canada for a secondary diagnosis. A secondary diagnosis was associated with higher rates of continuing care compared with a primary diagnosis (10.6%-24.2% vs 4.6%-12.1%). CONCLUSION: This study demonstrated that the mean LOS associated with ABSSSI due to MRSA in Canada was minimally 7 days. Clinical management strategies, including medication management, which might facilitate hospital discharge, have the potential to reduce hospital LOS and related economic burden associated with ABSSSI caused by MRSA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".