Utility of a Pulmonary TB diagnostic Algorithm to Guide Testing and Airborne Infection Isolation
Bibliographic record
Abstract
Background: Patients with suspected pulmonary TB tuberculosis (PTB) often require scarce airborne isolation rooms; minimizing use depends on clinician understanding of sputum and bronchoscopic test characteristics. Limited knowledge can lead to over-testing and unnecessary isolation days, straining hospital resources. Objective: Evaluate the impact of a PTB screening algorithm on reducing unnecessary testing and excess isolation days in patients with low to moderate pre-test probability. Methods: The study occurred 2022–2024 at a 1,286-bed tertiary care hospital in Toronto, Ontario (~880 TB cases annually). Inclusion criteria included inpatients placed on airborne isolation for suspected PTB with orders for either ≥3 expectorated sputa, ≥1 induced sputum, bronchoscopy, or combinations thereof. Patients with suspected Mycobacterium avium complex were excluded. A positive case is TB PCR or culture positive. Harm is defined as PTB exposure due to premature discontinuation of isolation. The algorithm recommended clinicians to collect a single induced sputum for low/moderate-risk patients with additional testing reserved for high-risk cases. Results: A total of 1,152 samples were collected from 747 patients; 513 expectorated sputa (44%), 194 induced sputa (16.8%), 445 bronchoscopies (38.6%). The median isolation duration was 6 days and the turnaround time for results ranged from 3–11 days. The positivity rate was 0.2% for performing expectorated sputum first (1/513), 2.5% for performing induced sputum (3/118) first, and 1.8% for BAL performed first (3/169). When comparing repeated induced sputum testing, all the samples were positive from the first specimen (Figure 2). Conclusion: These findings illustrate the real-world implications of using a single induced sputum to rule out PTB in low/moderate pre-testing probability patients, potentially leading to the reduction in airborne isolation days. No added harm via patient exposures was detected with the use of this algorithm.
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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 | 0.002 | 0.015 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".