Board 372 - Research Abstract The Other End
Bibliographic record
Abstract
Introduction/Background Massive hemorrhages are infrequent but life threatening complications of pediatric trauma and obstetrical cases. Expertise and effective communication is key among all team members actively involved in managing such events. Collaboration with blood bank technologists and hematologists responsible in assisting the team with prompt preparation and delivery of blood products further contributes to optimal patient care. We aimed to evaluate communication skills and expertise when preparing and delivering blood products in cases of massive haemorrhages using in-situ simulation and telemedicine. Methods Prospective observational study held at the blood bank and simulation lab of a tertiary mother-child health care facility in 2012. Participants were nurses, respiratory technicians, anesthetists, obstetricians, emergency physicians, intensivists, hematologists and blood bank technologists. Pediatric emergency/intensive care and obstetrical/anesthesia teams were submitted to high fidelity (HF) simulated pediatric trauma and post-partum massive hemorrhage scenarios respectively (SimbabyTM (Laerdal) and NoelleTM (Gaumard)) in the simulation lab. Concomitantly, blood bank technologists were videotaped and telephone conversations between participants at the simulation lab and blood bank technologists were recorded for review. If hematologists were consulted, they were called back by the blood bank chief technologist and were asked to answer questions relevant to the application of the massive hemorrhage protocol (MHP). Prior to the first simulation, blood bank technologists were asked to read the MHP and were individually met to answer questions. The MHP was explained to the hematologists during a formal group presentation and they had access to it at any time during the course of the study. All participants were observed initially and during the post session, two weeks later. A blinded independent trained rater reviewed all sessions and assessed performances. Blood bank technologists were evaluated using a checklist derived by transfusion experts rating expertise and key communication skills necessary when preparing and delivering blood products to teams involved in massive haemorrhages. Haematologists were evaluated using a questionnaire developed by transfusion security experts exploring their ability to assist and communicate with blood bank technologists and clinicians when dealing with the choice of blood products or compatibility issues. Means and standard deviations of scores on checklists and questionnaires were calculated for all observations. Results A total of 8 blood bank technologists, 8 haematologists and 62 healthcare professionals involved in 8 interdisciplinary teams (4 obstetrics/anaesthesia and 4 paediatric emergency/intensive care) participated in the study. Blood bank technologists scored on average 78% (range 61%-92%) and 76% (range 62 to 100%) in expertise and communication skills checklists during trauma and post-partum simulations, respectively. Haematologists rightly refused blood specimens to determine blood type in 57% (4/7) of the cases (discrepant ABO/Rh blood group with previous ABO group Results). Their ability to choose blood products or to substitute them was observed in 71% (5/7) of participants. When challenged by the fact that an incompatible blood product was delivered to the bleeding patient, only 37.5% (3/8) asked for phenotype analysis of products already transfused and those being prepared and no one advised the team caring for the patient of this fact and of the possibility of a haemolytic transfusion reaction. Conclusion Blood bank technologists were considered to be relatively well prepared and possess the necessary expertise and communication skills to prepare and deliver blood products to obstetrical and paediatric trauma teams dealing with massive haemorrhages. For haematologists, knowledge gaps were identified and additional training will be mandatory to ensure proficiency when assisting teams dealing with such emergencies. Using in-situ simulation at the blood bank and telemedicine for haematologists, in addition to HF interdisciplinary team simulations occurring in a simulation lab, can further contribute to improving performances of all professionals actively involved in a massive haemorrhage crisis situation. Disclosures None.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.551 | 0.335 |
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".