Do we need blood transfusion in elective infrarenal abdominal aortic aneurysm repair
Bibliographic record
Abstract
he discovery of the human immune deficiency virus, the risk of transmission of hepatitis C virus and the immunosuppressive effect of blood transfusion made the surgeons use every effort to avoid or minimize the use of blood and it’s products. It has been a supposition that any aortic surgery has to have a form of blood transfusion method available routinely,1 and most of the studies now are focusing on the autologous blood transfusion especially the cell saver to decrease or eliminate the need for allogenic blood.2 Unfortunately, autologous blood transfusion methods are expensive and in fact their routine use in elective infrarenal abdominal aortic aneurysm (EIAAA) repair, which constitute approximately 80% of aortic surgery was not shown to be cost effective. In this observational study, our aim was to evaluate our method of using meticulous surgical technique and avoiding hypothermia to decrease the rate of blood transfusion in EIAAA repair and to identify preoperative predictors. The charts of 72 patients were reviewed. All underwent EIAAA repair consecutively at our institution over a 3-year-period from January 1997 to December 1999. One surgical team using the same protocol did all repairs. All patients were cross-matched with 4 units of blood preoperatively. A standard midline laparotomy incision was used. Minimal aortic dissection, distal to the left renal vein and proximal to both common iliac arteries was carried out. All patients were given 5,000u of heparin 5 minutes before clamping, and reversed with protamine sulfate after the release of clamps. The comorbid conditions, preoperative hemoglobin (Hb), intraoperative hemodynamics, blood loss and an intraoperative Hb level less than 9, all were used as basis for transfusion. Patient’s age, sex, vascular risk factors and the aneurysm characteristics by computed tomography scan for size, calcifications and iliac involvement, were recorded. Pre and postoperative Hb, discharge Hb, type of repair, blood loss, hospital stay, perioperative morbidities and mortalities were compared between patients who received blood and those who did not. Seventy-two patients underwent repair of EIAAA over 3 years. Average age was 74 with the range of 54-89 years. Seventeen patients were more than 80 years old. Fifty-five (73%) of patients were males. There were no significant differences between transfused and non-transfused patients in risk factors. Thirty-nine (54%) patients had tube repair, 28 (39%) had aortobi-iliac bypass and 5 (7%) had aortobifemoral bypass. No significant difference Minor
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".