Three-point transfusion risk score in hepatectomy
Bibliographic record
Abstract
BACKGROUND: Perioperative red blood cell transfusions are required in up to 23 per cent of patients undergoing hepatectomy. Previous research has developed three transfusion risk scores to assess risk of perioperative red blood cell transfusion. Here, the performance of these transfusion risk scores was evaluated in a multicentre cohort of patients who underwent hepatectomy and compared with that of a simplified transfusion risk score. METHODS: A database of patients undergoing hepatectomy at four specialized centres between 2008 and 2012 was developed. External validity was assessed by discrimination and calibration. Discrimination was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). Calibration was evaluated by the degree of agreement between predicted and actual red blood cell transfusion probabilities. A simplified transfusion risk score using variables common to the three models was created, and discrimination and calibration were evaluated. RESULTS: There were 1287 patients included in this study, with 341 (26·5 per cent) receiving a red blood cell transfusion. Discriminative ability was similar between the three transfusion risk scores, with AUCs of 0·66-0·68 and good calibration. A new three-point risk score was developed based on factors present in all models: haemoglobin 12·5 g/dl or less, primary liver malignancy and major resection (at least 4 segments). Discriminative ability and calibration of the three-point model were similar to those of the three existing models, with an AUC of 0·66. CONCLUSION: The three-point transfusion risk score simplifies assessment of perioperative transfusion risk in hepatectomy without sacrificing predictive ability.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".