Use of Genetic Distance as a Measure of Ongoing Transmission of <i>Mycobacterium tuberculosis</i>
Bibliographic record
Abstract
The stability of the genotypic marker IS6110, used to define the epidemiology of Mycobacterium tuberculosis, is one of the most important factors influencing the interpretation of DNA fingerprint data. We propose that evolved strains should be considered together with clustered strains to represent chains of ongoing transmission. For the present study we used a large set of fingerprint data for strains collected between 1992 and 1998 from residents of a community with a high incidence of tuberculosis in Cape Town, South Africa. Interstrain genetic distances were calculated by counting the banding pattern mismatches in the IS6110 DNA fingerprints of different isolates. These data demonstrate that the propensity to change by one or two bands is independent of the IS6110 copy number. Hence, the genetic distance between pairs of isolates can be simply expressed as the number of differences in the banding patterns. From this foundation, a data set which identifies newly evolved strains has been generated. Inclusion of these evolved strains into various molecular epidemiological calculations significantly increased the estimate of ongoing transmission in this study setting. The indication is that nearly all cases of tuberculosis in this community are due to ongoing transmission. This has important implications for tuberculosis control, as it indicates that the control measures used at present are unable to reduce the level of transmission. This technique may also be applicable to the study of low-incidence tuberculosis outbreaks as well as the analysis of epidemiological data from other disease epidemics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".