The Effect of Preoperative Anemia on Blood Transfusion Outcomes in Major Head and Neck Cancer Surgery
Bibliographic record
Abstract
Background/objectives: Major head and neck oncologic surgeries requiring microvascular reconstruction frequently result in complications such as perioperative blood transfusion (PBT). Not only are blood products overutilized and associated with risks, but preoperative anemia is both a modifiable and predisposing factor for PBT. Our objective was to assess risk factors for PBT and determine a high-risk preoperative hemoglobin to inform transfusion stewardship practices. Methods: Patients that underwent head and neck cancer free flap reconstruction (n = 363) between 2012 and 2019 were included. Univariable and multivariable analyses evaluated predictors of PBT. Results: Overall, 11% of patients were anemic and 19% were transfused. Mean preoperative hemoglobin was significantly lower in the PBT group (128 g/L vs. 145 g/L, p < 0.0001). In our multivariable model, lower preoperative hemoglobin (odds ratio [OR] = 0.94), higher T stage (OR = 2.65), and lower body mass index (BMI) (OR = 0.89) increased the odds of PBT. Adjusting for staging and BMI, the OR of PBT was increased below 120 g/L hemoglobin. Higher mean units of PBT were administered for hemoglobin below 150 g/L with a large inflection below 120 g/L (p < 0.0001). Conclusions: Low preoperative hemoglobin is the strongest predictor of PBT in major head and neck cancer surgery. Recognizing and managing anemia is essential in surgical planning.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".