Estimation of Pancreas Weight from Donor Variables
Bibliographic record
Abstract
Previous studies have identified several donor factors affecting the outcome of islet isolation. Pancreas weight has not been considered as a donor selection criterion, because a value cannot be obtained prior to organ procurement. However, a larger pancreas will likely contain a higher number of islets. Therefore, the prediction of pancreas weight would be helpful in donor selection, benefiting cost and efficiency of the islet isolation laboratory. The purpose of this study was to investigate normal pancreas weight in cadaveric donors and identify pancreas weight predictors from demographic data of cadaveric organ donors. We retrospectively analyzed data on pancreas weight from 354 cadaveric donors with respect to gender, age, body weight, body height, body mass index (BMI), and body surface area (BSA). In men, pancreas weight correlated more closely with body weight than with age, height, or BMI. BSA was as strong a correlate of pancreas weight as body weight. In women, pancreas weight had a similar pattern of relationships, with generally lower correlation coefficients. On the basis of the observation of gender-specific pancreas weight difference in elderly donors, stepwise multiple linear regression analyses were conducted separately for younger (< or =40 years) and elderly (> or =41 years) donors. In younger donors, body weight and age were the major predictors of pancreas weight [pancreas weight (g) = 4.355 + 0.742 x body weight (kg) + 0.837 x age (years) (R2 = 0.564, p < 0.001)]. In contrast, pancreas weight of elderly donors was best predicted by BSA and gender [pancreas weight (g) = -17.624 + 60.036 x BSA (m2) - 7.152 x gender (R2 = 0.372, p < 0.001; "gender": 1 = female, 0 = male)]. Pancreas weight was found to be positively associated with pre- and postpurification islet yields. These formulae should contribute to the estimation of pancreas weight, and thus improve donor selection for islet isolation and transplantation.
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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 | 0.004 |
| 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) | 0.001 | 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".