Il carcinoma pancreatico
Bibliographic record
Abstract
Pancreatic cancer is the fourth leading cause of death for neoplasm in western countries. Surgery still remains the treatment of choice although almost 80% of patients are not resectable at diagnosis because of liver metastases and the 5-year overall survival of patients treated with surgery is only (20%). During the last two decades we have witnessed an overall improvement in terms of survival, mostly due to the advances in therapy and strategies for a more accurate patient selection for surgery. Specific preoperative criteria, mostly linked to the biological features of the tumour, have been proposed to better identify those patients who will benefit from surgical resection, such as duration of symptoms and serum level of CA19-9 in resectable disease. Oncological therapy plays a central role in the management of pancreatic cancer. In patients undergone surgical resection, adjuvant therapy might increase the overall survival and reduce the rate of early relapse. Patients with locally advanced pancreatic cancer can be treated with neoadjuvant treatment. Chemotherapy or chemoradiotherapy are usually used with the aim to downstage the disease but whether one specific strategy or drug is the treatment of choice is still under debate.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.014 |
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".