Obstetrical transfusion medicine knowledge among faculty and trainee obstetricians: a prospective knowledge assessment study
Bibliographic record
Abstract
OBJECTIVES: To evaluate the current state of transfusion medicine (TM) knowledge among obstetricians using a valid assessment tool. BACKGROUND: Transfusion issues are common in obstetrical patients. METHODS: Knowledge topics were identified and rated by experts in obstetrics, anaesthesia, haematology and TM using a modified Delphi method. A knowledge assessment tool was developed and validated during pilot testing. The assessment tool, consisting of 15 multiple choice questions, was administered electronically to members of the Society of Obstetricians and Gynaecologists of Canada (SOGC). RESULTS: A total of 192 SOGC members completed the assessment tool: 121 faculty obstetricians and 71 trainees. The average score was 65·8% ± 15·5. Scores for faculty were higher than trainees (68·9% ± 13·5 vs 60·6% ± 17·2; P < 0·001). Respondents performed well on questions related to red blood cell (RBC) transfusion and anaemia management but had lower scores on questions related to non-RBC transfusion and management of alloantibodies and fetomaternal haemorrhage (FMH) testing. There was no improvement in scores with increasing trainee level, years of practice, hours of formal TM training or experience with massive haemorrhage. Only self-rated knowledge was associated with scores ['no knowledge' or 'beginner' 63·1% ± 15 vs 'intermediate' or 'advanced' 68·9% ± 13·3 (P = 0·007)]. Of the respondents, 93·8% felt additional training in TM would be helpful. CONCLUSIONS: Overall knowledge assessment scores indicate the need for educational intervention, particularly with respect to non-RBC blood product use, management of FMH and management of pregnancies complicated by alloantibodies. The study also demonstrated a desire for additional TM training.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".