Liver Abscesses in Cancer Patients Associated with Poor Prognosis: A Single Center Experience
Bibliographic record
Abstract
Background: Liver abscesses are uncommon, but they are associated with a hospital mortality rate of 10% in patients without cancer. However, its relevance in cancer patients in terms of survival and length of hospitalization is unknown. Patients and Methods: We conducted an observational study in order to analyze the impact of liver abscesses in hospitalized cancer patients. Eligible patients were adults with a solid tumor, received antineoplastic treatment and were hospitalized in our department with a diagnosis of liver abscesses. Primary end-points of the study were overall survival (OS) and mean hospital stay (MHS). Secondary end-points were the description of risk factors of liver abscesses, isolated microorganisms and the specific treatment. Results: In our study the incidence of liver abscesses in cancer patients was 1.18%. Median age was 63 years, 60% were men, and the vast majority of patients had an advanced bilio-pancreatic tumor (85%). With a median follow-up of 18 months, median OS was 6 months (95% CI: 1-11); and OS probability at 18-month was 19%. MHS was 27.2 days (95% CI: 20.3-40.7). Risk factors for developing liver abscesses were: a) bilio-pancreatic cancers; b) chemotherapy administration; c) severe malnutrition; and d) biliary drainage/prosthesis. Ten patients (50%) had bacteremia, and the most common isolated microorganism was E.coli (30%). Percutaneous drainage of abscesses was performed in 40% of the patients, and all patients were treated with broad-spectrum antibiotics. Conclusions: Although liver abscesses were uncommon, they were associated with a prolonged hospitalization and poor outcome in cancer patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".