Use and Dissemination of the Brisbane 2000 Nomenclature of Liver Anatomy and Resections
Bibliographic record
Abstract
INTRODUCTION: The Brisbane 2000 Nomenclature of Hepatic Anatomy and Resections was created to standardize terminology in an area, which previously was characterized by redundant and confusing terms. The purpose of this study was to evaluate the use and dissemination of the nomenclature 10 years after its introduction. METHODS: Two strategies were used to evaluate implementation of the terminology. The first depended on an examination of terms used to describe the anatomy and resection of one half of the liver over the 20-year period from 1990 to 2009. The second approach evaluated the use of the terms "section," "sectionectomy," and "trisectionectomy," which, in reference to the liver, are unique to the Brisbane 2000 Nomenclature. RESULTS: The use of the Brisbane 2000 terms "right and left hemihepatectomy/hepatectomy" increased dramatically versus the use of the discarded terms "right and left hepatic lobectomy" after the Nomenclature was introduced in 2000. This was especially true in the Americas and Asia where the terms were used in less than 50% of papers from 1990 to 1999 but reached 80% utilization by 2006. Likewise, use of the terms "section," "sectionectomy," and "trisectionectomy" increased sharply especially in between 2006 and 2009. CONCLUSIONS: The Brisbane terminology is being adopted worldwide but its adoption is still incomplete.
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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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".