An international survey of maximum surgical blood ordering schedule creation and compliance
Bibliographic record
Abstract
Background and Objectives Maximal surgical blood order schedules ( MSBOS ) are prepared to assist surgeons and anaesthesiologists with pretransfusion test orders. The literature on the prevalence and usefulness of MSBOS in international settings is lacking. We developed a survey to understand the prevalence of MSBOS in hospitals and to analyse compliance with MSBOS recommendations. Method and Materials A survey tool was developed by the Biomedical Excellence for Safer Transfusions ( BEST ) collaborative. The survey link was distributed to BEST members who were encouraged to forward the link to colleagues. Survey respondents were asked to contribute data regarding their hospital's MSBOS and MSBOS compliance. Results There were 174 completed surveys. Greater than half of respondents did not have an MSBOS . The total number of beds was significantly different ( P = 0·04) in hospitals with (N = 81) or without (N = 93) an MSBOS . Less than a quarter of respondents felt that their MSBOS was being followed despite widespread access to the MSBOS . Over 60% of type and screen orders were MSBOS compliant; however, just 42% of cross‐match orders were compliant. There was a significant difference between the number of red‐blood‐cell units returned to the blood bank when comparing MSBOS compliant and MSBOS non‐compliant cross‐match orders (227 versus 788 units, respectively, P = 0·0026). Conclusions An MSBOS could be a useful tool to help with pretransfusion test orders; however, the majority of respondents lacked a MSBOS . In hospitals with a MSBOS , it was reported to be under‐utilized, which might contribute to the low compliance rates seen for cross‐match orders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".