Pilot-Optimizing Transfusion Therapy in Critically Ill Children With Anemia: A Pilot Multicenter Electronic Health Record-Enabled Randomized Controlled Trial on Red Blood Cell Transfusion in Critically Ill Children
Bibliographic record
Abstract
OBJECTIVES: To evaluate the feasibility of conducting a large international electronic health record-enabled randomized controlled trial (RCT) designed to compare two strategies for RBC transfusion (RBCT) in almost all anemic critically ill children, and the ability to support data abstraction from electronic medical data monitoring systems (eMDMSs). DESIGN: Two-arm parallel design pilot RCT. SETTING: Four university-affiliated PICUs in Canada, France, and United Kingdom. PATIENTS: Non-cyanotic critically ill children with hemoglobin (Hb) less than or equal to 9.5 g/dL. INTERVENTIONS: Participants were randomly allocated to a restrictive strategy (no RBCT if Hb ≥ 7.0 g/dL) or to usual care (clinician discretion for RBCT). MEASUREMENTS AND MAIN RESULTS: Feasibility outcomes were recruitment, adherence, separation of pre-RBCT Hb, and electronic data extraction from eMDMS. The proportion of patients with Hb less than or equal to 9.5 g/dL who were eligible for consent to participate in Pilot-Optimizing Transfusion Therapy in Critically Ill Children With Anemia (P-OpTTICCA) was 83% (feasibility criterion: ≥ 80%). We enrolled 120 patients, 63 in the restrictive and 57 in the usual care arms. We reached the planned recruitment rate (≥ 2 participants/wk in sites with > 800 admissions/yr, ≥ 1 in other sites). The pre-RBCT Hb concentration was 6.6 ± 7 and 6.7 ± 1.3 g/dL in the restrictive and usual care arms, respectively (separation = 0.1 g/dL; desired difference ≥ 1.0 g/dL). The pre-transfusion Hb concentration was greater than or equal to 7.0 g/dL for 6 of 20 RBCT (30%) given to patients allocated to the restrictive arm (success criterion for protocol adherence: < 20%). There were two protocol deviations, but no protocol violation. Using eMDMS, 87.7% of the 390 data elements in the case report form were extracted and/or calculated electronically (success criterion: > 80%). CONCLUSIONS: P-OpTTICCA demonstrated the feasibility of recruitment, adherence, and electronic data extraction, but we did not get a good separation of pre-RBCT Hb. Future trials need to clearly define transfusion Hb thresholds in both trial arms (NCT03871244).
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".