Extended Screening Costs Associated With Selecting Donors for Fecal Microbiota Transplantation for Treatment of Metabolic Syndrome-Associated Diseases
Bibliographic record
Abstract
Abstract Background Knowledge of the impact of the gut microbiome on conditions other than Clostridium difficile infection has been rapidly increasing, and the potential usefulness of fecal microbiota transplantation (FMT) in these indications is being explored. The need to exclude donors with an increased risk of these diseases has left uncertainties regarding the cost and feasibility of donor screening. The aim of this study was to compare our experience to other donor-screening programs and report the costs associated with establishing a donor-screening program, for the treatment of metabolic syndrome-related conditions. Methods Forty-six potential donors (PDs) had their medical histories and physical examinations undertaken by a physician. Blood, stool, and urine were screened for 31 viral, bacterial, fungal, and protozoan agents in addition to biochemical characteristics. The price of advertising, doctor’s visits and diagnostic tests were calculated to determine the cost of finding a donor. Results Of the PDs screened, 5 of 46 passed the history, examination, blood, stool, and urine tests. The most common reasons for exclusion included a body mass index >25 or the detection of Blastocystis hominis, Dientamoeba fragilis, or Helicobacter pylori. Four of five eligible donors had subsequent travel or illness that contraindicated donation, so only 1 of 46 PDs was suitable. The total cost for finding a single suitable donor was $15190 US dollars. This screening was performed in Canada, and costs in the United States would be substantially higher. Conclusions New potential therapeutic uses for FMT have created a demand for stricter exclusion criteria for donors. This study illustrates that screening many individuals to find a donor and the subsequent associated costs may make central processing and shipment a more reasonable alternative.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".