Blood loss during vascular access cannulation: Quantification using the weighed gauze and drape method
Bibliographic record
Abstract
Anemia is an important complication of chronic renal disease, with a significant impact on the morbidity, quality of life, and mortality in this group of patients. Inadequate erythropoietin production, reduced life span of erythrocytes in uremic serum, bone marrow suppression by uremic toxins, chronic inflammation, and contaminants in the water treatment unit are recognized etiological causes of anemia in chronic kidney disease patients. Little attention has been paid to possible contributions of small but continual loss of blood during vascular access cannulation for hemodialysis in these patients. The aim of this study is to quantify the volume of blood loss during femoral vein cannulation in patients on hemodialysis. The average volume of blood loss during femoral cannulation was evaluated using a simple and inexpensive procedure of deriving volume of blood lost, from the weights of soaked gauze and drape during the access cannulation procedure. The mean blood loss per procedure during femoral cannulation was 36.52 mL+/-2.70 SD, with a range of 6.47 to 100.20 mL. The calculated average weekly loss in patients on thrice-weekly dialysis schedule is 109.56 mL of blood, with a monthly loss of 438.24 mL. Cumulative blood loss during femoral vein cannulation appears to be significant. Its contribution in the sustenance of anemia in hemodialysis patients deserves further evaluation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".