Randomized Study of Aprotinin Effect on Transfusions and Blood Loss in Primary THA
Bibliographic record
Abstract
A projected increase in total hip arthroplasties, shortfalls in blood availability, and awareness of complications of transfusion make blood management in orthopaedic surgery important. In a multicenter, randomized, double-blind, placebo-controlled study, we hypothesized use of aprotinin would reduce blood transfusions (any and allogeneic) and blood loss in total hip arthroplasty. Using an intent-to-treat approach, we recruited 393 patients stratified by preoperative autologous blood donation or none and then randomized them to receive aprotinin (176 patients receiving a 10,000 kallikrein inhibitor units [KIU] test dose, 2 million KIU load, 0.5 million KIU per hour) or placebo (177 patients). We assessed patients at baseline; postoperative days 1, 2, 3, and 7 (or discharge); and 6 +/- 2 weeks. Primary efficacy was percentage of patients having blood transfusion through day 7 or discharge. We based safety on reported adverse events. Aprotinin reduced transfusions by 46% (30 of 176 versus 56 of 177 patients). Aprotinin reduced the total number of any blood units and the number of allogeneic blood units transfused relative to placebo (48 versus 109 units and 30 versus 72 units, respectively). Serious complications were similar in the two groups (placebo, 11%; aprotinin, 10%). Our data suggest full-dose aprotinin is safe and effective in decreasing blood transfusion in total hip arthroplasty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".