Blood Conservation in Neurosurgery: Erythropoietin and Autologous Donation
Bibliographic record
Abstract
BACKGROUND: Neurosurgery may involve significant blood loss and frequently requires allogeneic red blood cell (RBC) transfusion. Preoperative recombinant erythropoietin (EPO) may be used to improve erythroid status and recovery, and used either alone or in combination with preoperative autologous donation (PAD) it may reduce exposure to allogeneic RBC. We wished to study the use of EPO with and without PAD and the risk of RBC transfusion in neurosurgery. METHODS: Using a retrospective case-control design, 57 patients who received EPO preoperatively were matched 2:1 for age, sex, year of surgery, and International Classification of Diseases code most responsible for surgery (three were excluded because of stringent matching criteria, leaving 54 cases and 108 comparison subjects). Thirty-two cases participated in PAD. Medical and anesthetic records as well as laboratory investigations were reviewed and extracted. RESULTS: Allogeneic RBC exposure was identical for EPO cases and comparison subjects (18.5%). Concomitant PAD and EPO did not reduce allogeneic RBC exposure (21.9%), and resulted in a greater number of RBC units transfused. Last recorded hemoglobin levels suggested that autologous RBCs were not more liberally used. Patients who engaged in PAD and EPO suffered from iatrogenic anemia. A significant proportion (58.6%) of the autologous RBCs was ultimately not used and discarded. CONCLUSION: Further research is needed to determine the efficacy of EPO in neurological surgery. PAD does not appear to reduce the risk of allogeneic RBC transfusion, despite concomitant EPO. Indeed, PAD resulted in iatrogenic anemia and increased transfusion requirements. The cost-effectiveness of blood conservation efforts in neurosurgery deserves additional research.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".