Characterization of Coinfections in Patients with COVID-19
Bibliographic record
Abstract
Background: Little is known about coinfections in patients with COVID-19, with antibiotics often initiated empirically. Objectives: To determine the rates and characteristics of early and late coinfections in COVID-19 patients and to characterize the use of anti-infective agents, especially antibiotics. Methods: This retrospective chart review involved patients with COVID-19 who were admitted to Lions Gate Hospital (Vancouver, British Columbia) between January 1 and June 30, 2020. Data were extracted from electronic medical records, and descriptive statistics were used to analyze the data. Results: Of the 48 patients admitted during the study period, 10 (21%) were determined to have coinfections: 3 (6%) had early coinfections and 7 (15%) had late coinfections. Early empiric use of antibiotics was observed in 32 (67%) patients; for 29 (91%) of these 32 patients, the therapy was deemed inappropriate. Patients with coinfections had longer hospital stays and more complications. Conclusions: Despite low rates of early coinfection, empiric antibiotics were started for a majority of the patients. Most late coinfections occurred in patients in the intensive care unit who required mechanical ventilation. Patients with coinfections had poorer outcomes than those without coinfections. RÉSUMÉ Contexte : On sait peu de choses sur les co-infections chez les patients atteints de COVID-19, les médicaments antibiotiques étant souvent initiés de manière empirique.Objectifs : Déterminer les taux et les caractéristiques des co-infections précoces et tardives chez les patients atteints de COVID-19 et caractériser l’utilisation d’anti-infectieux, en particulier les antibiotiques. Méthodes : Cet examen rétrospectif des dossiers portait sur des patients atteints de COVID-19 qui ont été admis à l’hôpital Lions Gate, à Vancouver (Colombie-Britannique), entre le 1er janvier et le 30 juin 2020. Les données ont été extraites des dossiers médicaux électroniques et des statistiques descriptives ont été utilisées pour analyser les données. Résultats : Sur les 48 patients admis au cours de la période d’étude, 10 patients (21 %) présentaient des co-infections : 3 patients (6 %) avaient des co-infections précoces et 7 (15 %), des co-infections tardives. Une utilisation empirique précoce d’antibiotiques a été observée chez 32 patients (67 %); pour 29 de ces 32 patients (91 %), le traitement a été jugé inapproprié. Les séjours à l’hôpital des patients co-infectés étaient plus longs et ils présentaient davantage de complications. Conclusions : Malgré de faibles taux de co-infection précoce, des antibiotiques empiriques ont été instaurés pour la majorité des patients. La plupart des co-infections tardives sont survenues chez des patients de l’unité de soins intensifs nécessitant une ventilation mécanique. Les résultats des patients avec co-infections étaient moins bons que ceux sans co-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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".