Secondary analysis of data from the HeLiX trial regarding the association between estimated blood loss and post-hepatectomy outcomes: towards the definition of a minimal clinically significant difference
Bibliographic record
Abstract
Estimated blood loss (EBL) during hepatectomy is commonly reported and associated with short- and long-term postoperative outcomes such as morbidity, recurrence, and survival1. However, EBL lacks standardized benchmarks and thresholds to define clinically meaningful differences. Small statistical differences (as low as 85–100 ml) have been reported, though their clinical significance is uncertain. The concept of a minimal clinically significant difference (MCSD), as the smallest change that meaningfully alters patient management, offers a framework to address this gap2,3. The aim of this study was to evaluate the association between standardized EBL and postoperative outcomes to inform the development of an EBL MCSD for hepatectomy. A secondary analysis of data from the HeLiX RCT (NCT02261415), which enrolled adults undergoing hepatectomy at 11 centres in Canada and the USA to examine the effect of tranexamic acid (TXA) on perioperative transfusions (see the Supplementary material)4, was performed. The exposure was standardized EBL, calculated from gauze weight and suction volume minus irrigation5. Outcomes were 30-day red blood cell transfusion (RBCT) (yes/no) and 30-day major morbidity (Clavien–Dindo grade III–V). Multivariable logistic regression models with restricted cubic splines examined the association between EBL increments and outcomes adjusted for potential confounders. Adjusted relative risks (aRRs) with 95% confidence intervals were calculated for sequential 100 ml increments in EBL.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".