COVID-19 Clinical Profiles and Fatality Rates in Hospitalized Patients Reveal Case Aggravation and Selective Co-Infection by Limited Gram-Negative Bacteria
Bibliographic record
Abstract
Bacterial co-infections may aggravate COVID-19 disease, and therefore being cognizant of other pathogens is imperative. We studied the types, frequency, antibiogram, case fatality rates (CFR), and clinical profiles of co-infecting-pathogens in 301 COVID-19 patients. Co-infection was 36% (n = 109), while CFR was 31.2% compared to 9.9% in non-co-infected patients (z-value = 3.1). Four bacterial species dominated, namely, multidrug-resistant Klebsiella pneumoniae (37%, n = 48), extremely drug-resistant Acinetobacter baumannii (26%, n = 34), multidrug-resistant Eschericia. coli (18.6%, n = 24), and extremely drug-resistant Pseudomonas aeruginosa (8.5%, n = 11), in addition to other bacterial species (9.3%, n = 12). Increased co-infection of K. pneumoniae and A. baumannii was associated with increased death rates of 29% (n = 14) and 32% (n = 11), respectively. Klebsiella pneumoniae was equally frequent in respiratory and urinary tract infections (UTI), while E. coli mostly caused UTI (67%), and A. baumannii and P. aeruginosa dominated respiratory infections (38% and 45%, respectively). Co-infections correlated with advance in age: seniors ≥ 50 years (71%), young adults 21–49 years (25.6%), and children 0–20 years (3%). These findings have significant clinical implications in the successful COVID-19 therapies, particularly in geriatric management. Future studies would reveal insights into the potential selective mechanism(s) of Gram-negative bacterial co-infection in COVID-19 patients.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".