Application and Interpretation of an Interferon‐Gamma Release Assay: Results of an Audit in a Canadian Centre
Bibliographic record
Abstract
BACKGROUND: Interferon-gamma release assays (IGRAs) are newly approved for diagnosing latent tuberculosis infection (LTBI). An internal audit was conducted to review the use of a newly implemented IGRA at the Hôpital du Sacré-Coeur de Montréal (Montréal, Québec) to evaluate its concordance with Canadian recommendations and its implication on diagnosis. METHODS: From April 2007 to January 2009, all Quantiferon TB Gold In-Tube (QFT, Cellestis inc, USA) tests performed in at the Hôpital du Sacré-Coeur de Montréal were retrieved. Strategies used to investigate LTBI and clinical interpretation of test results were compared with the local algorithm, which is derived from the current national guidelines. RESULTS: A total of 200 patients tested with QFT were included in the analysis. LTBI investigation and QFT testing were considered to be appropriate in 87.5% and 66.5% of patients, respectively. Overall, 67 QFT tests were performed inappropriately; 25 were performed when a LTBI investigation was not indicated and 42 were performed whe LTBI interpretation was possible with the result of the tuberculin skin test alone. Among the 175 patients investigated appropriately for LTBI, 49 QFT tests (28%) were interpreted incorrectly; 32 patients (at high risk of developing active tuberculosis) had a positive tuberculin skin test and a negative QFT result wrongly interpreted as being negative for LTBI and 13 patients should have undergone further LTBI investigations. CONCLUSION: Globally, the present study revealed that there are discrepancies on how the IGRA was employed and interpreted in a Montreal hospital and that strict compliance to the guidelines could significantly reduce errors in interpretation.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".