Bibliographic record
Abstract
To the Editor—We thank Jouhten et al for their interest in our work [1] and appreciate their complementary findings in the effects of fecal transplantation on antibiotic resistance genes (ABR) in recipients. Their research, using quantitative polymerase chain reaction array for 85 ABR genes in recurrent Clostridium difficile infection (RCDI) patients before and after fecal microbiota transplant (FMT) further corroborates our findings that FMTs can reduce the number of ABR genes in RCDI patients. Their discussion focuses on the importance of donor screening and the possibility of transferring ABR genes from a donor into a recipient. We agree that donor selection and screening is critical in order to minimize any potential transmission of infectious agents, and yet there is no consensus on how this is done. There are some general recommendations when it comes to donor selection criteria. However, what is included in donor testing has been variable, although serology for viral hepatitis, human immunodeficiency virus, syphilis, stool testing for culture and sensitivity, ova and parasite and Clostridium difficile are seen as the bare minimum [2, 3]. In our FMT program, we are fortunate to have dedicated donors who are relatively young, in their 30s, who have received few courses of antibiotics throughout their lives. We follow donor testing proposed by Bakken et al [4] every 4 months and further screen for the clinically important antibiotic-resistant organism such as vancomycin-resistant Enterococci and methicillin-resistant Staphlococcus aureus. It is not known how extensive or how frequent donor testing needs to be done, because cases linking FMT to the possible transmission of norovirus, cytomegalovirus, and Blastocystis hominis have been reported, further highlighting the importance of rigorous and thorough donor screening [5–7].
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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 | 0.005 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.129 | 0.060 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".