Management of Shiga toxin producing <i>Escherichia coli</i>‐infected children: A multi‐national, multi‐specialty survey
Bibliographic record
Abstract
AIM: Research has highlighted the potential role that hydration status may play in predicting outcomes in Shiga toxin-producing Escherichia coli (STEC)-infected children. Because little is known about the management of STEC-infected children in the pre-haemolytic uremic syndrome phase, we compared paediatric emergency medicine and nephrologist-stated management approaches to STEC-infected children. METHODS: Members of the Pediatric Emergency Research Canada (PERC; n = 228), the Pediatric Emergency Medicine Collaborative Research Committee (PEM CRC; n = 221) and the Canadian Association of Pediatric Nephrologists (CAPN; n = 66) were surveyed. Five individualised e-mail requests containing a link to a 42-question web-based survey were sent to eligible participants. RESULTS: Of 496 potentially eligible participants, 276 (56%) submitted complete survey responses. In children with classic features of STEC infection, baseline haemoglobin/haematocrit is obtained by 54% of PERC, 41% of PEM CRC and 83% of CAPN members (P < 0.001), and baseline renal function is obtained by 51% of PERC, 38% of PEM CRC and 83% of CAPN members (P < 0.001). Intravenous fluids are more often recommended by nephrologists (28%) compared with PEM physicians (7%), P < 0.001. In children with known E. coli O157:H7 infection, nephrologists more commonly recommend clinical follow-up (P = 0.003), complete blood counts (P < 0.001) and renal function/electrolyte testing (P < 0.001). Intravenous fluid administration and admission are more commonly recommended by nephrologists (P = 0.03 and P < 0.001, respectively). CONCLUSION: Compared with paediatric nephrologists, paediatric emergency medicine physicians are less likely to perform baseline and follow-up blood tests and to administer intravascular volume expansion in children at risk of, and with confirmed, E. coli O157:H7 infection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".