Improving follow-up testing in children with Shiga toxin-producing Escherichia coli through provision of a provider information sheet
Bibliographic record
Abstract
The aim of this study was to improve follow-up laboratory testing for children infected by Shiga toxin-producing Escherichia coli (STEC) through the provision of an information sheet to healthcare providers in the province of Alberta, Canada. An information sheet recommending the performance of laboratory tests, every 24-48h until 3 days after diarrhoea resolves or the platelet count stabilises or begins to rise, was sent to all physicians who ordered a STEC-positive stool test as of 1 November 2016. The information sheet was only distributed to physicians in one of the province's five healthcare delivery zones (i.e. intervention zone). Medical records for children aged <18 years with laboratory confirmed STEC-positive stool samples between November 2014 and November 2018 were reviewed to determine the performance of recommended laboratory tests. Post-intervention, follow-up testing in all categories increased significantly for cases that occurred in the intervention zone, with odds ratios (OR) ranging from 3.02 (95% CI: 1.35-6.78) to 3.94 (95% CI: 1.70-9.16) when compared with pre-intervention. No increase in any of the laboratory testing categories was detected outside of the intervention zone. The provision of a targeted information sheet to healthcare providers improved the monitoring of STEC-infected children.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".