Additional file 1 of Evaluating the potential for respiratory metagenomics to improve treatment of secondary infection and detection of nosocomial transmission on expanded COVID-19 intensive care units
Bibliographic record
Abstract
Additional file 1: Supplementary Methods and Tables S1-S8. Table S1. List of pre-defined pathogen and reference source for each pathogen. Table S2. All non-pathogenic organisms identified in all respiratory samples processed with clinical metagenomics (above pre-defined thresholds). Table S3. Clinical characteristics and results of routine microbiological tests performed on intubated COVID-19 patients across 7 linked dedicated COVID-19 intensive care units on Guy’s and St Thomas’ hospital sites during the first wave of the COVID-19 pandemic. Table S4A. Sequencing metadata for all respiratory samples processed with clinical metagenomics. Table S4B. Negative controls run with each batch of samples sequenced. Table S5. Phenotypic resistance reported by culture and resistance genes reported by clinical metagenomics in all culture-positive samples after 2 hours of sequencing. Table S6. Microbiology, PCR and clinical metagenomics results for all samples processed in this study for the identification of Aspergillus fumigatus. Table S7A-D. Klebsiella pneumoniae and Corynebacterium striatum alignment for outbreak analysis. Table S8A-E. Performance reported after testing different parameters on training set for pathogen identification. The number of True Positive (TP), False Positive (FP), True Negative and False Negative (TN) samples as well as sensitivity, specificity along and calculated Youden’s Index ((sensitivity+specificity)-1) are presented.
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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 | 0.003 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.844 | 0.128 |
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".