Risk Factors and Prognostic Factors in GBC
Bibliographic record
Abstract
Background: Gallbladder cancer (GBC) is a rare entity with a poor prognosis, usually discovered late due to nonspecific symptoms; therefore, over the last years, attention has been focused on identifying the risk factors for developing this malignancy in order to provide an early diagnosis, as well as new prognostic factors in order to modulate the long-term evolution of such cases. The aim of this review is to discuss both major risk factors and prognostic factors in GBC for a better understanding and integration of relevant and currently available information. Methods: A literature search was performed using Cochrane Library, PubMed, Google Scholar, Elsevier, and Web of Science; studies published after the year of 2000, in English, were reviewed. Results: Over time, risk factors associated with the development of GBC have been identified, which outline the profile of patients with this disease. The most important prognostic factors in GBC remain TNM staging, safety margin, and R0 status, along with perineural invasion and lymphovascular invasion. Both the technique and experience of the surgeons and a pathological examination that ensures final staging are particularly important and increase the chances of survival of the patients. Conclusions: improvements in surgical techniques and pathological analyses might provide better and more consistent guidance for medical staff in the management of patients with GBC.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".