Transfusion in orthopaedic surgery
Bibliographic record
Abstract
Aims Orthopaedic surgeries are complex, frequently performed procedures associated with significant haemorrhage and perioperative blood transfusion. Given refinements in surgical techniques and changes to transfusion practices, we aim to describe contemporary transfusion practices in orthopaedic surgery in order to inform perioperative planning and blood banking requirements. Methods We performed a retrospective cohort study of adult patients who underwent orthopaedic surgery at four Canadian hospitals between 2014 and 2016. We studied all patients admitted to hospital for nonarthroscopic joint surgeries, amputations, and fracture surgeries. For each surgery and surgical subgroup, we characterized the proportion of patients who received red blood cell (RBC) transfusion, the mean/median number of RBC units transfused, and exposure to platelets and plasma. Results Of the 14,584 included patients, the most commonly performed surgeries were knee arthroplasty (24.8%), hip arthroplasty (24.6%), and hip fracture surgery (17.4%). A total of 10.3% of patients received RBC transfusion; the proportion of patients receiving RBC transfusions varied widely based on the surgical subgroup (0.0% to 33.1%). Primary knee arthroplasty and hip arthroplasty, the two most common surgeries, were associated with in-hospital transfusion frequencies of 2.8% and 4.5%, respectively. RBC transfusion occurred in 25.0% of hip fracture surgeries, accounting for the greatest total number of RBC units transfused in our cohort (38.0% of all transfused RBC units). Platelet and plasma transfusions were uncommon. Conclusion Orthopaedic surgeries were associated with variable rates of transfusion. The rate of RBC transfusion is highly dependent on the surgery type. Identifying surgeries with the highest transfusion rates, and further evaluation of factors that contribute to transfusion in identified at-risk populations, can serve to inform perioperative planning and blood bank requirements, and facilitate pre-emptive transfusion mitigation strategies. Cite this article: Bone Jt Open 2021;2(10):850–857.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".