Review of 16S and ITS Direct Sequencing Results for Clinical Specimens Submitted to a Reference Laboratory
Bibliographic record
Abstract
We evaluated the performance of 16S and internal transcribed spacer (ITS) region amplification and sequencing of rDNA from clinical specimens, for the respective detection and identification of bacterial and fungal pathogens. Direct rDNA amplification of 16S and ITS targets from clinical samples was performed over a 4-year period and reviewed. All specimens were from sterile sites and submitted to a reference laboratory for evaluation. Results of 16S and ITS were compared to histopathology, Gram and/or calcofluor stain microscopy results. A total of 277 16S tests were performed, with 64 (23%) positive for the presence of bacterial DNA. Identification of an organism was more likely in microscopy positive 16S samples 14/21 (67%), compared to 35/175 (20%) of microscopy negative samples. A total of 110 ITS tests were performed, with 14 (13%) positive. The yield of microscopy positive ITS samples, 9/44 (21%), was higher than microscopy negative samples 3/50 (6%). Given these findings, 16S and ITS are valuable options for culture negative specimens from sterile sites, particularly in the setting of positive microscopy findings. Where microscopy results are negative, the limited sensitivity of 16S and ITS in detecting and identifying an infectious agent needs to be considered.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".