136: The Other End: Evaluation of Blood Bank Technologists and Hematologists During a Massive Hemorrhage Simulation Project
Bibliographic record
Abstract
Massive hemorrhages (MH) are life-threatening complications of trauma and obstetrical cases. Expertise and effective communication is key among all team members involved. Collaboration with blood bank technologists (BBT) and hematologists assisting teams with prompt preparation and delivery of blood products further contributes to optimizing care. To evaluate communication and expertise when preparing and delivering blood products during MH cases using in situ simulation. Prospective observational study held at a blood bank (BB) and simlab of a tertiary care facility. Participants were BBTs and hematologists and pediatric emergency/critical care and obstetrical/anesthesia teams submitted to high fidelity (HF) simulated pediatric trauma and post-partum MH scenarios, respectively. BBT were videotaped at the BB and conversations with teams at the simlab were recorded. If hematologists were consulted, the chief BBT questioned them on issues relevant to MH.BBT were evaluated by a blinded trained independent rater using a checklist rating expertise and key communication skills necessary when preparing and delivering blood products to teams involved in MH. Hematologists were rated using a questionnaire exploring their ability to assist and communicate with BBTs and clinicians dealing with choosing blood products or compatibility issues. Means and SDs of scores on checklists and questionnaires were calculated. Participants were eight BBT, eight hematologists and 62 healthcare professionals involved in eight interdisciplinary teams (four obstetrics/anaesthesia and four pediatric emergency/critical care). BBT scored on average 78% (61% to 92%) and 76%(62% to 100%) in expertise and communication checklists (trauma and post-partum simulations, respectively). Hematologists rightly refused blood specimens to determine blood type in 57% (four of seven) (discrepant ABO/Rh blood group with previous ABO group results).Their ability to choose or substitute blood products was observed in 71% (five of seven).When challenged by the fact that an incompatible blood product was delivered to the patient, 37.5% (three of eight) asked for phenotype analysis of products already transfused and those being prepared and no one advised teams or of the possibility of a hemolytic transfusion reaction. BBT were considered relatively ready and possess necessary expertise and communication skills to prepare and deliver blood products to teams dealing with MH. For hematologists, knowledge gaps were identified and additional training is mandatory to ensure proficiency when assisting teams with such emergencies. Using in situ simulation at the BB and hematologists in addition to HF interdisciplinary team simulations at a simlab can further add to improving performances of all professionals actively involved in MH crisis situations.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".