The Influence of Additional Treatments on the Survival of Patients Undergoing Transarterial Radioembolization (TARE)
Bibliographic record
Abstract
The aim of this study was to present our preliminary experience with transarterial radioembolization (TARE) using Yttrium-90 (90Y), compare the cancer-specific survival (CSS) of patients with hepatocellular carcinoma (HCC) and colorectal cancer (CRC) liver metastases undergoing TARE, and investigate the influence of additional treatments on CSS. Our database was interrogated to retrieve patients who had undergone TARE using Yttrium-90 (90Y) glass or resin microspheres. Kaplan–Meier curves and the log-rank test were employed to conduct survival analysis for the different groups (p < 0.05). Thirty-nine patients were retrieved (sex: 27 M, 12 F; mean age: 63.59 ± 15.66 years): twenty-three with hepatocellular carcinoma (HCC) and sixteen with CRC liver metastasis. Globally, the patients with HCC demonstrated a significantly longer CSS than those with CRC liver metastasis (22.64 ± 2.7 vs. 7.21 ± 1.65 months; p = 0.014). Among the patients with CRC liver metastasis, those receiving TARE and additional concomitant treatments (n = 10) demonstrated a longer CSS than the CRC patients receiving only TARE (9.97 ± 2.21 vs. 2.59 ± 0.24 months; p = 0.06). In the HCC group, there was a trend of a longer CSS in patients (n = 8) receiving TARE and additional treatments (27.89 ± 3.1 vs. 17.69 ± 3.14 months; p = 0.15). Patients with HCC seem to achieve a longer survival after TARE compared to patients with CRC liver metastases. In patients with CRC liver metastases, the combination of TARE and additional concomitant treatments may improve survival.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".