Calculation of the Stability of the IS <i>6110</i> Banding Pattern in Patients with Persistent <i>Mycobacterium tuberculosis</i> Disease
Bibliographic record
Abstract
The interpretation of molecular epidemiologic data of Mycobacterium tuberculosis infection is dependent on the understanding of the stability and evolutionary characteristics of the DNA fingerprinting marker used to classify clinical isolates. This study investigated the stability of the IS6110 banding pattern in serial tuberculosis isolates collected from patients resident in an area with a high incidence of tuberculosis. Evolutionary changes were observed in 4% of the strains, and a half-life (t(1/2)) of 8.74 years was calculated, assuming a constant rate of change over time. This rate may be composed of a high rate of change seen during the early disease phase (t(1/2) = 0.57 years) and a low rate of change seen in the late disease phase (t(1/2) = 10.69 years). The early rate probably reflects change occurring during active growth prior to therapy, while the low late rate may reflect change occurring during or after treatment. We demonstrate that the calculation of these rates is strongly influenced by the time interval between onset of disease and sputum sampling. These calculations are further complicated by partial replacement of the original strain population, resulting in the sporadic appearance of clonal variants in sputum specimens. Therefore, the true extent of genetic diversity may be underestimated within each host, thereby influencing molecular epidemiological data used to establish transmission chains.
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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 | 0.011 |
| 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".