Cryoablation and Immunotherapy: An Enthralling Synergy for Cancer Treatment
Bibliographic record
Abstract
As less invasive options for surgical tumor removal, minimally invasive ablative techniques have gained popularity. Several solid tumors are being treated with cryoablation, a non-heat-based ablation technique. Cryoablation data in comparison over time demonstrates better tumor response and faster recovery. Combining cryosurgery with other cancer therapies has been explored to improve the cancer-killing process. Cryoablation with the combination of immunotherapy, results in a robust and efficient attack on the cancer cells. This article focuses on investigating the ability of cryosurgery to create a strong antitumor response when combined with immunologic agents resulting in a synergetic effect. To achieve this objective, we combined cryosurgery with immunotherapy using Nivolumab and lpilimumab. Five clinical cases of lymph node, lung cancer, bone, and lung metastasis were followed and analyzed. In this series of patients, percutaneous cryoablation and addressing immunity agents were technically feasible. In the follow-ups, there appeared to be no radiological evidence of new tumor development.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".