Use of sputum microbiology for identifying lower respiratory tract microbial colonization in patients with obstructive airway disorders
Bibliographic record
Abstract
INTRODUCTION Lower respiratory tract microbial colonization caused by bacterial pathogens in patients with obstructive airway disorders, such as asthma, bronchiectasis and chronic obstructive pulmonary disease (COPD), can lead to worsened clinical outcomes and increased costs associated with the disease to both the healthcare system and patients. Thus, efforts to identify the etiology of the infection are critical to effective treatment. The analysis of expectorated sputum collected from individuals can be diagnostic and allow for noninvasive sampling, causing it to historically be considered the preferred method of diagnosis. However, discourse surrounding the diagnostic utility with current and easier to access techniques including invasive procedures, such as bronchoscopy, have led to changing patterns of assessment, which can also impact costs to the health system.METHODS A systematic review of literature from Embase, Medline and Scopus databases was conducted to compile records regarding the use of sputum microbiology in patients with one of the aforementioned obstructive airway disorders but without pneumonia. We compared two broad techniques within sputum microbiology: sputum culture and sequencing.RESULTS We found that while sequencing often identified specific species at a higher frequency, sputum culture methods identified a larger diversity of bacterial species.CONCLUSIONS The current literature has a disproportionate representation of COPD and patients over the age of 60. There is a lack of consensus regarding what is deemed a conventional method. Future studies should evaluate the role of sequencing, including costs, when considering implementation into clinical laboratories, particularly in a single payer system such as in Canada and the United Kingdom.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".