Identification of <i>Mycobacterium</i> spp. by Using a Commercial 16S Ribosomal DNA Sequencing Kit and Additional Sequencing Libraries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Current methods for identification of Mycobacterium spp. rely upon time-consuming phenotypic tests, mycolic acid analysis, and narrow-spectrum nucleic acid probes. Newer approaches include PCR and sequencing technologies. We evaluated the MicroSeq 500 16S ribosomal DNA (rDNA) bacterial sequencing kit (Applied Biosystems, Foster City, Calif.) for its ability to identify Mycobacterium isolates. The kit is based on PCR and sequencing of the first 500 bp of the bacterial rRNA gene. One hundred nineteen mycobacterial isolates (94 clinical isolates and 25 reference strains) were identified using traditional phenotypic methods and the MicroSeq system in conjunction with separate databases. The sequencing system gave 87% (104 of 119) concordant results when compared with traditional phenotypic methods. An independent laboratory using a separate database analyzed the sequences of the 15 discordant samples and confirmed the results. The use of 16S rDNA sequencing technology for identification of Mycobacterium spp. provides more rapid and more accurate characterization than do phenotypic methods. The MicroSeq 500 system simplifies the sequencing process but, in its present form, requires use of additional databases such as the Ribosomal Differentiation of Medical Microorganisms (RIDOM) to precisely identify subtypes of type strains and species not currently in the MicroSeq library.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it