Evaluation of Anatomically Designed Flocked Rectal Swabs for Use with the BioFire FilmArray Gastrointestinal Panel for Detection of Enteric Pathogens in Children Admitted to Hospital with Severe Gastroenteritis
Bibliographic record
Abstract
Diagnosing diarrheal disease is difficult in part due to challenges in obtaining and transporting a bulk stool specimen, particularly in resource-limited settings. We compared the performance of flocked rectal swabs to that of traditional bulk stool samples for enteric pathogen detection using the BioFire FilmArray gastrointestinal panel in children admitted to four hospitals in Botswana with community onset severe gastroenteritis. Of the 117-matched flocked rectal swab/stool pairs, we found no significant difference in pathogen detection rates between the flocked rectal swab samples and traditional bulk stool sampling methods for any bacterial (168 versus 167, respectively), viral (94 versus 92, respectively), or protozoan (18 versus 18, respectively) targets. The combination of flocked rectal swab samples with FilmArray testing allows for the rapid diagnosis of infectious gastroenteritis, facilitating a test-and-treat approach for infections that are life-threatening in many resource-limited settings. The culture recovery rates for bacterial pathogens utilizing this approach need to be assessed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".