Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a fatal type of cancer with an increasing incidence rate in North America. The only curative procedure of this disease is the Whipple procedure, which is restricted only to those that received an early diagnosis. The remainder of the patients are informed of a dismal prognosis and undergo palliative care through systemic chemotherapy. Multiple modalities are involved in the staging and diagnosis of this disease. However, there seems to be a controversy regarding a gold standard or whether a gold standard exists. Additionally, there are various emerging techniques that warrant heightened sensitivity and specificity in their designated modalities. Transabdominal ultrasound that is most commonly used as the first line of imaging for patients with epigastric pain is found to be virtually insensitive to neoplasms that have a size of 2 cm or less, limiting its application. However, sonographers could resort to contrasts and elastography to increase the conspicuity of the neoplasms. Moreover, endoscopic ultrasound has shown to be a promising imaging modality with an unprecedented degree of sensitivity to tumors with a diameter less than 1.5 cm. The sensitivity and specificity values of MDCT, MRI, and PET were found to be comparable. The main conclusions consist of the fact that EUS is a highly sensitive test that should be accompanied by MRI, MDCT, PET, or TUS to increase its specificity. Lastly, empathetic communication is vital not only for patient comfort but also to improve the quality of the imaging assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".