Pigment Visibility on Rectal Swabs Used To Detect Enteropathogens: a Prospective Cohort Study
Bibliographic record
Abstract
Data are lacking regarding the impact of visible pigment on rectal swab diagnostic accuracy. We describe the test characteristics of rectal swabs with and without pigment in children with gastroenteritis. Between December 2014 and September 2017, children (age, <18 years) with ≥3 episodes of vomiting and/or diarrhea in a 24-h period and symptoms for <7 days were enrolled through two pediatric emergency departments and from a province-wide nursing telephone advice line in Alberta, Canada. Specimens were analyzed by employing nucleic acid amplification panels. The primary outcomes were the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the rectal swabs, with stool specimen results being used as the reference standard. An enteropathogen was detected in 76.0% (1,399/1,841) of the paired specimens. A total of 54.4% (1,001/1841) of the swabs had visible pigment. The respective enteropathogen detection characteristics of swabs with and without visible pigment were as follows: 92.2% (95% confidence interval [CI], 90.0%, 94.0%) versus 83.7% (95% CI, 80.5%, 86.4%) for sensitivity, 94.3% (95% CI, 90.5%, 96.6%) versus 91.2% (95% CI, 86.3%, 94.5%) for specificity, 97.9% (95% CI, 96.4%, 98.8%) versus 96.5% (95% CI, 94.5%, 97.8%) for PPV, and 80.9% (95% CI, 76.0%, 85.1%) versus 65.8% (95% CI, 60.0%, 71.1%) for NPV. Processing of swabs without visible pigment would increase the rate of identification of positive swabs from 50.0% (682/1,365) to 88.3% (1,205/1,365). There is a modest decrease in the reliability of a negative test on swabs without evidence of pigment, but the overall yield is significantly greater when they are not excluded from testing. Hence, rectal swabs without visible feces should not be routinely rejected from testing.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".