Evaluation of data quality in a laboratory-based surveillance of <i>M. tuberculosis</i> drug resistance and impact on the prevalence of resistance: France, 2004
Bibliographic record
Abstract
In France, surveillance of anti-tuberculosis drug resistance is performed by the Azay-Mycobacteria network, representing 30% of all culture-positive cases. We sought to validate administrative and clinical data gathered by the network in 2004 and to produce corrected resistance rates accounting for the observed misclassification. We reviewed a 10% sample of patients' records diagnosed in 2004 and measured the agreement between controlled data and data collected by the network by using the kappa (kappa) statistic. A re-sampling bootstrap-based method was used to investigate the impact of bias found on resistance rates. Most of data collected by the network, such as demographic data, and country of birth had an excellent agreement (kappa>0.8) with controlled data. The concordance was good (kappa>0.6) for HIV status and tuberculosis site. The only variable slightly discordant with controlled data was prior history of treatment (kappa=0.52). However, after correcting crude resistance rates for the observed misclassification, all estimated rates were within confidence intervals based on reported rates. This validation study is in favour of a good quality of data produced by the network, even though corrected rates are slightly different from observed rates. Therefore, data collected through the network may be used for policy making and tuberculosis programme evaluation. However, improvement in data collection regarding prior history of treatment should be considered.
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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 | 0.164 | 0.274 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".