Transfusion medicine curricular content for general pediatricians and pediatric subspecialists: A national multi‐specialty Delphi consensus study
Bibliographic record
Abstract
BACKGROUND: Although pediatric residents frequently order blood products, transfusion medicine (TM) education is both limited and unstandardized during postgraduate training. Using Delphi methodology, this study aimed to identify and prioritize which pediatric TM curricular topics are most important to inform postgraduate training in TM for general pediatricians and pediatric subspecialists. METHODS: A national panel of experts iteratively rated potential curricular topics, on a 5-point scale, to determine their priority for inclusion within a TM curriculum. After each round, responses were analyzed. Topics receiving a mean rating <3/5 were removed from subsequent rounds and remaining topics were resent to the panel for further ratings until consensus was achieved, defined as Cronbach α ≥ 0.95. At conclusion of the Delphi process, topics rated ≥4/5 were considered core curricular topics, while topics rated ≥3 to <4 were considered extended topics. RESULTS: Forty-five TM experts from 17 Canadian institutions and 12 subspecialties completed the first Delphi round and 31 completed the second. Fifty-seven potential curricular topics were generated from a systematic literature review and Delphi panelists. Two survey rounds were completed before consensus was achieved. Seventy-three topics in six domains reached consensus: 31 core curricular topics and 42 extended topics. There were no significant differences in ratings between TM and non-TM specialists. DISCUSSION: A multispecialty Delphi panel reached consensus in identification of curricular topics for pediatric resident physicians. These results set the stage to develop a pediatric TM curriculum that will be foundational for pediatric trainees to enhance learning and improve transfusion safety.
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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 | 0.113 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".