Identification of bacterial and fungal pathogens directly from clinical blood cultures using whole genome sequencing
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
Bloodstream infections are a major cause of morbidity and mortality worldwide. Early administration of appropriate antimicrobial therapy can improve patient survival and prevent antimicrobial resistance (AMR). Whole genome sequencing (WGS) can provide information for pathogen identification, AMR prediction and sequence typing earlier than current phenotypic diagnostic methods. WGS was performed on 97 clinical blood specimens and matched culture isolate pairs. Specimen/isolate pairs were MLST sequence-typed and further characterization was performed on Streptococcus species. WGS correctly identified 91.7% of clinical specimens and 93.2% of matched isolates representing 35 different microbial species. MLST types were assigned for 89.9% of matched cultures and 21.7% of blood specimens, with higher success for blood culture specimens extracted within 3 days (52% characterized) than 7 days (9.3%). This study demonstrates the potential use of WGS for identification and characterization of pathogens directly from blood culture specimens to facilitate timely initiation of appropriate antimicrobial therapies.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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