RECENT UPDATES IN THE ROLE OF MULTI-DETECTOR COMPUTED TOMOGRAPHY IN EVALUATION OF PANCREATIC CANCER RESECTABILITY
Bibliographic record
Abstract
INTRODUCTIONPancreatic cancer has one of the worst prognosis among all malignancies and is projected to become the second leading cause of cancer-related deaths in certain regions. It is the fourth leading cause of cancer-related deaths worldwide. pancreatic cancer has a low 5-year survival rate of 2% to 9%, which remains consistent across both high-income and low- to middle-income countries. This survival rate also varies by location and country, but it never exceeds 10%.Adenocarcinoma is the most common type of pancreatic cancer . The name 'silent killer' has been given to this cancer because it progresses silently, has late clinical signs, and grows rapidly. . AIM OF THE WORKThe main goal of our study was to assess the recent updates in the role of MDCT in prediction of resectability of pancreatic cancerSUBJECTS AND METHODS Target population: The study was carried out on 42 patients with approved manifestations of cancer pancreas who admitted to Alexandria main university hospital and Gamal Abdel Naser insurance hospital for diagnosis and management.Before CT imaging, all individuals were subjected to: I. Informed written consent.II. Full history and clinical examination.III. General and abdominal examinations by the referral clinician.IV. Laboratory investigations.V. Imaging techniques:
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".