Quantitative Assessment of Pancreatic Texture Using a Durometer: A New Tool to Predict the Risk of Developing a Postoperative Fistula
Bibliographic record
Abstract
BACKGROUND: Pancreatic texture is one of the key predictors of postoperative pancreatic fistula (POPF) after pancreatoduodenectomy (PD). Currently, the "gold standard" for assessment of pancreatic texture is surgeon's subjective evaluation through manual palpation. AIM: To evaluate a new "durometer" that is able to assess quantitatively the pancreatic stiffness by measuring its elastic module (i.e., the resistance offered by the pancreatic stump when elastically deformed expressed in mPa). METHODS: Measurements were obtained from the pancreatic remnant during 138 consecutive PDs performed at the Department of General and Pancreatic Surgery-The Pancreas Institute, University of Verona Hospital Trust. Values were correlated to clinical features and, in particular, with the senior surgeon's evaluation of pancreatic texture (hard/soft). Sixteen beating-heart donors were used as a control group to assess the stiffness of a non-pathologic pancreas. Univariate analysis was performed for the assessment of POPF predictors. RESULTS: Durometry allowed segregating between non-pathologic, soft and hard pancreas according to surgeon's evaluation (mean values 111 vs. 196 vs. 366 mPa, p < 0.01). There were no significant differences in stiffness with regard to histology, BMI, and neoadjuvant therapy. Larger tumors (>20 mm) and male sex were associated with greater stiffness on univariate analysis. Pancreatic texture, pancreatic duct size, BMI, prior neoadjuvant therapy, and histology were predictors of POPF. Patients who developed POPF showed a lesser stiffness (178 vs. 261 mPa, p = 0.05). CONCLUSION: Assessment of pancreatic stiffness using a durometer correlated with the surgeon's evaluation of pancreatic texture. Measurement of pancreatic parenchymal stiffness is reliable and correlates with the development of POPF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".