Oral and Nasal Decontamination for COVID-19 Patients: More Harm Than Good?
Bibliographic record
Abstract
To the Editor The recent article by Dexter et al1 provides much-needed guidance for anesthesiologists and other health care workers involved with the perioperative management of confirmed or suspected coronavirus disease 2019 (COVID-19) patients. The unprecedented nature of the pandemic has lead to confusion regarding the safest infection control and operating room management strategies. Furthermore, the evidence base is rapidly evolving or is extrapolated from historical experience, making best practices difficult to discern for frontline clinicians and institutional leaders. The review provided by Dexter et al1 gives a concise 5-step road map for evidence-based infection control in the operating room. Although many of the suggestions seem to have clear merit, the proposed method for patient decolonization may be counterintuitive.1 While some evidence exists for nasal decontamination in preventing surgical-site infection in Staphylococcus aureus carriers,2,3 they present no substantive evidence that nasal/oral decontamination would actually reduce viral transmission. Perhaps more importantly, application of nasal povidone-iodine could induce sneezing, paradoxically increasing the spread of aerosolized viral particles, and a chlorhexidine mouth rinse might also risk inducing coughing (or at the very least some expectoration) which could also increase the risk of contamination. The theoretical benefit of decolonization with preoperative nasal povidone-iodine and chlorhexidine mouth rinse needs to balance with the potential risk of inducing aerosolizing complications, such that one does not increase the risk they are attempting to mitigate. Duncan Maguire, MDDepartment of AnesthesiologyPerioperative and Pain MedicineUniversity of ManitobaWinnipeg, Manitoba, Canada[email protected]
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".