Pathogen yield and antimicrobial resistance patterns of chronic rhinosinusitis patients presenting to a tertiary rhinology centre.
Bibliographic record
Abstract
OBJECTIVES: To examine the yield and resistance profile of pathogens in chronic rhinosinusitis (CRS) patients receiving culture-directed management and to pay particular attention to the prevalence of methicillin-resistant Staphylococcus aureus (MRSA) in this population. STUDY DESIGN: Retrospective review of a CRS microbiology database. PARTICIPANTS: Consecutive CRS patients seen at the St. Paul's Sinus Centre between June 2007 and August 2008. SETTING: Canadian tertiary sinus centre. MAIN OUTCOME MEASURE: To determine the pathogens isolated, the frequency of these pathogens, and their resistance profiles. RESULTS: The most common bacterial pathogens isolated were Staphylococcus aureus, accounting for 39% of cultured samples, followed by Haemophilus influenzae (29%), Pseudomonas aeruginosa (15%), Streptococcus pneumoniae (12%), and Moraxella catarrhalis (11%). Only three cases of MRSA were found, one in a patient with cystic fibrosis. CONCLUSION: MRSA does not appear to pose a significant risk of morbidity in our patient population. However, ongoing concern regarding the increasing prevalence of S. aureus and antimicrobial resistance in chronic sinonasal disease highlights the importance of using culture-directed antimicrobial therapy with the goal of minimizing future resistance patterns.
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".