Pre-surgical Nasal Decolonization of <i>Staphylococcus aureus:</i> A Health Technology Assessment.
Bibliographic record
Abstract
Background: , and patient preferences and values. Methods: in pre-surgical patients in Ontario. To contextualize the potential value of nasal decolonization, we spoke with people who had recently undergone surgery, some of whom had received nasal decolonization, and one family member of a person who had recently had surgery. We also engaged participants through an online survey. Results: carrier status before receiving nasal decolonization with mupirocin. The annual budget impact of publicly funding universal nasal decolonization in Ontario over the next 5 years ranges from a savings of $2.98 million in year 1 to a savings of $15.09 million in year 5. The annual budget impact of publicly funding targeted nasal decolonization ranges from an additional cost of $0.08 million in year 1 to an additional cost of $0.39 million in year 5.Our interview and survey respondents felt strongly about the value of preventing surgical site infections, and most favoured a universal approach. Conclusions: -related surgical site infections but increase the overall cost of treatment for the health care system. We estimate that publicly funding universal nasal decolonization using mupirocin combined with chlorhexidine body wash would result in a total cost savings of $45.08 million over the next 5 years, whereas publicly funding targeted nasal decolonization using mupirocin combined with chlorhexidine body wash would incur an additional cost of $1.17 million over the next 5 years.People undergoing surgery value treatments aimed at preventing surgical site infections.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".