Effect of chronic renal failure on postoperative mortality rate following arterial reconstruction
Bibliographic record
Abstract
Abstract Background Chronic renal failure (CRF) is both a risk factor for, and a consequence of, peripheral arterial disease. There is also increasing evidence that patients with CRF have a higher mortality rate following arterial reconstruction, but it is not known whether this is different for aneurysmal or occlusive disease. Methods Over 8 years 1718 consecutive arterial reconstructions performed under the care of one consultant were studied prospectively. Patients were defined as having CRF if at the time of surgery the serum creatinine measured more than 400 μmol 1−1, they were undergoing dialysis or they had received a renal transplant. The in-hospital postoperative mortality rate was compared between patients with and without CRF and analysed according to the urgency as well as the type of arterial reconstruction. Results Sixty-nine patients (4 per cent) undergoing arterial reconstruction over the study period were defined as having CRF. Sixteen (23 per cent) of these died following surgery (myocardial infarct seven, multiple organ failure three, stroke one, mesenteric thrombosis one, paraplegia one, respiratory failure one, pneumonia one, failure to thrive one), in comparison to 120 (7·3 per cent) of 1649 patients without CRF. The mortality rate in patients with CRF was highest in those who underwent urgent/emergency surgery and those who had reconstruction for occlusive disease. Conclusion Patients with CRF have a threefold increase in mortality rate following arterial reconstruction. Patients undergoing infrainguinal bypass are at particular risk and should be carefully selected and counselled.
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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 | 0.005 |
| 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) | 0.002 | 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".