Improving the treatment of pre-operative anemia in hepato-pancreato-biliary patients: a quality improvement initiative
Bibliographic record
Abstract
BACKGROUND: Pre-operative anemia is a common, but treatable, condition encountered by surgical patients. It has been associated with increased perioperative complications, length of stay, and blood transfusions. The aim of this project was to increase the treatment rate of pre-operative anemia to 75% of patients consented for major hepato-pancreato-biliary (HPB) surgery. METHODS: This was an interrupted time series study and a spread initiative from a similar project in a colorectal surgery population. Interventions included an anemia screening and treatment algorithm, standardized blood work, referral to a patient blood management program, and standardized oral iron prescriptions. The primary outcome measure was the change in pre-operative anemia treatment rate and the secondary outcome measure was the post treatment increase in hemoglobin. RESULTS: = 0.03). There was no significant increase or decrease in blood transfusions or mean number of red cell units transfused per patient. Screening rates for pre-operative anemia increased from 41.1 to 64.3% and appropriate referrals to the patient blood management program increased from 14.3 to 67.6%. CONCLUSIONS: This study demonstrates a small scale spread initiative focused on the treatment of pre-operative anemia. Although the goal to treat 75% of anemic patients was not reached, an effective referral pathway to an existing patient blood management program was developed, and a significant increase in the mean hemoglobin in anemic patients who have been treated pre-operatively was demonstrated.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".