McKay agar enables routine quantification of the ‘Streptococcus milleri’ group in cystic fibrosis patients
Bibliographic record
Abstract
The 'Streptococcus milleri' group (SMG) has recently been recognized as a contributor to bronchopulmonary disease in cystic fibrosis (CF). Routine detection and quantification is limited by current CF microbiology protocols. McKay agar was developed previously for the semi-selective isolation of this group. Here, McKay agar was validated against a panel of clinical SMG isolates, which revealed improved SMG recovery compared with Columbia blood agar. The effectiveness of this medium was evaluated by appending it to the standard CF sputum microbiology protocols in a clinical laboratory for a 6-month period. All unique colony types were isolated and identified by 16S rRNA gene sequencing. Whilst a wide variety of organisms were isolated, members of the SMG were the most prevalent bacteria cultured, and McKay agar allowed routine quantification of the SMG from 10(3) to >10(8) c.f.u. ml(-1) directly from sputum. All members of the SMG were detected [Streptococcus anginosus (40.7 %), Streptococcus intermedius (34.3 %) and Streptococcus constellatus (25 %)] with an overall prevalence rate of 40.6 % in our adult CF population. Without exception, samples where SMG isolates were cultured at 10(7) c.f.u. ml(-1) or greater were associated with pulmonary exacerbations. This study demonstrates that McKay agar can be used routinely to quantify the SMG from complex clinical samples.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".