A High Proportion of Novel Mycobacteria Species Identified by 16S rDNA Analysis Among Slowly Growing AccuProbe-Negative Strains in a Clinical Setting
Bibliographic record
Abstract
Sequencing of the 16S ribosomal DNA (rDNA) for identification of nontuberculous mycobacteria (NTM) has contributed to the establishment of more than 35 new species during the last decade. Increasingly, NTM are accepted as potential or proven pathogens. We identified, by 16S rDNA sequence analysis, slowly growing NTM isolates negative by AccuProbe (GenProbe, San Diego, CA) that previously were identified by using conventional biochemical techniques, to determine the accuracy of reporting AccuProbe-negative NTM prior to sequence-based identification. Of 82 strains, 30 were deemed novel. An attempt was made to determine the clinical importance of previously misidentified novel species. Clinical cases are described for a number of strains previously identified as Mycobacterium terrae complex, Mycobacterium scrofulaceum, and Mycobacterium avium complex. As sequence-based identification methods become more commonplace in clinical microbiology laboratories, there is a need to understand the significance of previously undescribed species, which often mimic and subsequently are identified as well-established species.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".