Physician Attitudes toward the Use of Fecal Microbiota Transplantation for the Treatment of Recurrent<i>Clostridium difficile</i>Infection
Bibliographic record
Abstract
BACKGROUND: Fecal microbiota transplantation (FMT) is a safe and effective, yet infrequently used therapy for recurrent Clostridium difficile infection (CDI). OBJECTIVE: To characterize barriers to FMT adoption by surveying physicians about their experiences and attitudes toward the use of FMT. METHODS: An electronic survey was distributed to physicians to assess their experience with CDI and attitudes toward FMT. RESULTS: A total of 139 surveys were sent and 135 were completed, yielding a response rate of 97%. Twenty-five (20%) physicians had treated a patient with FMT, 10 (8%) offered to treat with FMT, nine (7%) referred a patient to receive FMT, and 83 (65%) had neither offered nor referred a patient for FMT. Physicians who had experience with FMT (performed, offered or referred) were more likely to be male, an infectious diseases specialist, >40 years of age, fellowship trained and practicing in an urban setting. The most common reasons for not offering or referring a patient for FMT were: not having 'the right clinical situation' (33%); the belief that patients would find it too unappealing (24%); and institutional or logistical barriers (23%). Only 8% of physicians predicted that the majority of patients would opt for FMT if given the option. Physicians predicted that patients would find all aspects of the FMT process more unappealing than they would as providers. CONCLUSIONS: Physicians have limited experience with FMT despite having treated patients with multiple recurrent CDIs. There is a clear discordance between physician beliefs about FMT and patient willingness to accept FMT as a treatment for recurrent CDI.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".