A 10-year prospective surveillance of<i>Mycobacterium tuberculosis</i>drug resistance in France 1995–2004
Bibliographic record
Abstract
Drug resistance surveillance and trend monitoring resistance rates bring some insights into tuberculosis (TB) control. The current study reports the characteristics of TB and drug resistance during a 10-yr prospective surveillance of culture-positive TB in France. Data for the current study was collected from 1995-2004 via a sentinel network of laboratories from university hospitals that complied with the international recommendations for the surveillance of drug resistance. Susceptibility test results were performed in each individual laboratory. Data on 13,283 patients were collected during the 10-yr period, 49% of whom had been born in France, 10% were HIV co-infected and 8% had previously been treated. As expected, previously treated and HIV co-infected patients were more likely to harbour resistant strains, especially rifampicin (RMP)-resistant strains. Among new patients, the mean resistance rate to at least one drug was 8.8%, and there was an upward trend in resistance to isoniazid and RMP (0.8-1%) related to the increase in the proportion of patients who had been born outside of France (38-53%). Among previously treated patients, the mean resistance rate to one drug was 20.6% and there was no significant time trend in resistance rates. The sentinel network provided valuable data on trends regarding the characteristics of tuberculosis and on drug resistance rates and reinforced the interest of analysing data by country of birth and history of treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".