Optimising the yield from bronchoalveolar lavage on human participants in infectious disease immunology research
Bibliographic record
Abstract
Bronchoalveolar lavage (BAL) is becoming a common procedure for research into infectious disease immunology. Little is known about the clinical factors which influence the main outcomes of the procedure. In research participants who underwent BAL according to guidelines, the BAL volume yield, and cell yield, concentration, viability, pellet colour and differential count were analysed for association with important participant characteristics such as active tuberculosis (TB) disease, TB exposure, HIV infection and recent SARS-CoV-2 infection. In 337 participants, BAL volume and BAL cell count were correlated in those with active TB disease, and current smokers. The right middle lobe yielded the highest volume. BAL cell and volume yields were lower in older participants, who also had more neutrophils. Current smokers yielded lower volumes and higher numbers of all cell types, and usually had a black pellet. Active TB disease was associated with higher cell yields, but this declined at the end of treatment. HIV infection was associated with more bloody pellets, and recent SARS-CoV-2 infection with a higher proportion of lymphocytes. These results allow researchers to optimise their participant and end assay selection for projects involving lung immune cells.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".