Conventional Methods versus 16S Ribosomal DNA Sequencing for Identification of Nontuberculous Mycobacteria: Cost Analysis
Bibliographic record
Abstract
The clinical profile of nontuberculous mycobacteria (NTM) has been raised by the human immunodeficiency virus and AIDS pandemic. Different laboratory techniques, often molecular based, are available to facilitate the rapid and accurate identification of NTM. The expense of these advanced techniques has been questioned. At the National Reference Center for Mycobacteriology and the Health Sciences Center, University of Manitoba, in Winnipeg, Canada, we performed a direct cost analysis of laboratory techniques for commercial DNA probe-negative (Gen-Probe, Inc., San Diego, Calif.), difficult-to-identify NTM. We compared the costs associated with conventional phenotypic methodology (biochemical testing, pigment production, growth, and colony characteristics) and genotypic methodology (16S ribosomal DNA [rDNA] sequence-based identification). We revealed a higher cost per sample with conventional methods, and this cost varied with organism characteristics: $80.93 for slowly growing, biochemically active NTM; $173.23 for slowly growing, biochemically inert NTM; and $129.40 for rapidly growing NTM. The cost per sample using 16S rDNA sequencing was $47.91 irrespective of organism characteristics, less than one-third of the expense associated with phenotypic identification of biochemically inert, slow growers. Starting with a pure culture, the turnaround time to species identification is 1 to 2 days for 16S rDNA sequencing compared to 2 to 6 weeks for biochemical testing. The accuracy of results comparing both methodologies is briefly discussed. 16S rDNA sequencing provides a cost-effective alternative in the identification of clinically relevant forms of probe-negative NTM. This concept is not only useful in mycobacteriology but also is highly applicable in other areas of clinical microbiology.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".