The Challenging Scenario of Cancer Treatment for People with HIV: Clinical Experience with Immune Checkpoint Inhibitors
Bibliographic record
Abstract
Over the past decade, there has been a notable increase in the utilization of immune checkpoint inhibitors in cancer care, transforming the therapeutic landscape for several types of solid tumors. This development has not only expanded the indications for treatment but has also significantly influenced management strategies and prognostic outcomes for specific subsets of cancer patients. In contrast to the general population of cancer patients, individuals diagnosed with both HIV and cancer encounter significant differences in treatment approaches and outcomes. Consequently, this population demonstrates a significantly increased rate of specific mortality for several common types of cancer. Recent studies have reported significant insights into the use of immune checkpoint inhibitors among this patient group. However, the data remain insufficient, and there are still recognized barriers and limitations regarding the use of these agents in cancer patients. Real-world data and reports from clinical practice offer critical perspectives, enabling the sharing of clinical experiences and assisting in navigating complex management decisions. This report outlines two cases of patients with concurrent HIV and cancer who were administered ICIs in diverse clinical settings, highlighting the necessity of cooperation between oncologists and HIV specialists to provide patients with cutting-edge and increasingly tailored treatment options.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".