Correlation Between Liver Weight and Dimensions to Patient Demographics
Bibliographic record
Abstract
Background Treatment of hepatocellular carcinoma (HCC) often involves the use of targeted radiation therapy, chemotherapeutic agents, and percutaneous ethanol injections, but issues of underdosing and overdosing still prevail and dosimetric calculations are often suboptimal. Methods 33 cadavers donated to the University of British Columbia (UBC) Body Donation Program were examined, and data was collected on liver weight and dimensions. The individual’s demographics (height, weight) were obtained from the department’s database, and the liver demographics were correlated with the individuals’ demographics. Results Data was analyzed for correlations between body weight and liver weight, person’s height and the height of the liver along the right surface, body weight and the liver’s length along the superior surface, and body weight and the distance the liver extended left of the midline. A strong positive correlation was identified between body weight and liver weight, with r=0.7475 and p < 0.00001. All other results either concluded weak correlations, or were insignificant. Discussion The strong correlation identified between body weight and liver weight can be used as a potential additional tool during initial clinical examinations to aid dosimetric calculations of radiation and chemotherapy treatments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".