Understanding Bacterial Isolates in Blood Culture and Approaches Used to Define Bacteria as Contaminants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Interpretation of blood culture isolates is challenging due to a lack of standard methodologies for identifying contaminants. This problem becomes more complex when the specimens are from sick young infants, as a wide range of bacteria can cause illness among this group. METHODS: We used 43 key words to find articles published between 1970 and 2011 on blood culture isolates and possible contaminants in the PubMed database. Experts were also consulted to obtain other relevant articles. Selection of articles followed systematic methods considering opinions from more than 1 reviewer. RESULTS: After reviewing the titles of 3869 articles extracted from the database, we found 307 relevant to our objective. Based on the abstracts, 42 articles were selected for the literature review. In addition, we included 7 more articles based on cross-references and expert advice. The most common methods for differentiating blood culture isolates were multiple blood cultures from the same subject, antibiograms and molecular testing. Streptococcus pneumoniae, Hemophilus influenzae, Neisseria meningitidis and group A and B streptococcus were always considered as pathogens, whereas Bacillus sp., Diphtheroids, Propionibacterium and Micrococcus were commonly regarded as contaminants. Coagulase-negative staphylococci were the most frequent isolates and usually reported as contaminants unless the patient had a specific condition, such as long-term hospitalization or use of invasive devices (catheters). CONCLUSIONS: Inaccurate interpretation of blood culture may falsely guide treatment and also has long-term policy implications. The combination of clinical and microbiological knowledge, patient's clinical history and laboratory findings are essential for appropriate interpretation of blood culture.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it