IDDF2021-ABS-0086 Diversified aspects of donor screening for washed microbiota transplantation at china microbiota transplantation system
Bibliographic record
Abstract
<h3>Background</h3> Effective screening of donors can greatly improve the safety and efficacy of washed microbiota transplantation (WMT), which is of great concern to the public. fmtBank (www.fmtbank.org), a non-profit public project providing national rescue WMT, needs to meet the continuous demand for qualified fecal donors based on the automatic microfiltration machine. Therefore, we have established a step-by-step donor screening program and management process. <h3>Methods</h3> The qualification of 944 candidates to serve as potential WMT donors was assessed from 2015 to 2021 in Nanjing Medical University. Individuals interested in fecal donation should complete a three-stage donor-screening process, including questionnaire interview (IDDF2021-ABS-0086 Table 1), face-to-face interview and laboratory testing. <h3>Results</h3> Figure 1 and 2 shows the whole process of screening (IDDF2021-ABS-0086 Figure 1. Crowd demonstrated, IDDF2021-ABS-0086 Figure 2. Screening process). In total 898 valid questionnaires, the age was 20.77±1.45. Only 8.7% meet the inclusion and exclusion criteria. 82 potential donors who have passed the primary screening need to undergo a face-to-face interview with a trained physician, which selected 54 students to finish laboratory tests including blood, urine and fecal screening. Finally, 32 qualified donors were selected. Because the median time a donor actively donated stool in our center was 11.5 months, we defined stool donation for more than 6 months as a long-term donor. 10 students eventually became long-term donors. 32 donors provided 2009 times of fecal donations. As shown in Figure 3 (IDDF2021-ABS-0086 Figure 3. Correlation of fecal weight and amount of enriched washed microbiota), the fecal weight was not well correlated with the amount of enriched washed microbiota (95% CI, 0.62–0.67, P < 0.0001), r was 0.64, (IDDF2021-ABS-0086 Figure 4. Detailed exclusion criteria and proportions). <h3>Conclusions</h3> In our center, we selected 32 donors from 944 students, with a qualified rate of 3.4%. This result is similar to the qualified rate of donor screening reported in stool bank of the United States, Canada and South Korea. And we found that long-term donors have better microbiota output and compliance. The study found that it is feasible to screen qualified donors from a large sample by step-by-step screening. Through this programmed donor screening management process, convenient and repeatable donor screening can be realized.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".