Classification of unsequenced Mycobacterium tuberculosis strains in a high-burden setting using a pairwise logistic regression approach
Bibliographic record
Abstract
Over the past three decades, molecular epidemiological studies have provided new opportunities to investigate the transmission dynamics of Mycobacterium tuberculosis . In most studies, a sizable fraction of individuals with notified tuberculosis cannot be included, either because they do not have culture-positive disease (and thus do not have specimens available for molecular typing) or because resources for conducting sequencing are limited. A recent study introduced a regression-based approach for inferring the membership of unsequenced tuberculosis cases in transmission clusters based on host demographic and epidemiological data. This method was able to identify the most likely cluster to which an unsequenced strain belonged with an accuracy of 35%, although this was in a low-burden setting where a large fraction of cases occurred among foreign-born migrants. Here, we apply a similar model to M. tuberculosis whole-genome sequencing data from the Republic of Moldova, a setting of relatively high local transmission. Using a maximum cluster span of ~40 single nucleotide polymorphisms (SNPs) and a cluster size cutoff of n ≥10, we could best predict the specific cluster to which each clustered case was most likely to be a member with an accuracy of 17.2 %. In sensitivity analyses, we found that a more restrictive (~20 SNPs threshold) or permissive (~80 SNPs) threshold did not improve performance. We found that increasing the minimum cluster size improved prediction accuracy. These findings highlight the challenges of transmission inference in high-burden settings like Moldova.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".