Evaluating the Efficacy of Immunotherapy in Fragile Hospitalized Patients
Bibliographic record
Abstract
Background: Immunotherapy is the cornerstone of treatment for many cancers. The effectiveness of immunotherapy in hospitalized patients is unknown due to the exclusion of this fragile population from clinical trials. This study evaluates the efficacy of immunotherapy in fragile hospitalized patients. Method: We conducted a single-center retrospective study involving 49 patients who started an immunotherapy (IO) during a hospitalization or within 3 months after a hospitalization at the Centre Hospitalier de l’Université de Sherbrooke (CHUS). Efficacy analysis included objective response rate (ORR), overall survival (OS), and progression-free survival (PFS). Results: Immunotherapy resulted in 30.6% of all grades combined and 18.4% of grade three to four immune-related adverse events (irAE). Efficacy outcomes were inferior in the fragile cohort of patients with ORR of 38.9%, PFS of 2.8 months (95% CI [2.17–3.35]), and OS of 3.2 months (95% CI [1.60–4.84]). Performance status of ECOG three to four compared to ECOG zero predicts poor OS (HR 5.666 [1.207–26.594]; p = 0.028) and PFS (HR 4.136 [0.867–19.733]; p = 0.075). Fitness to receive four to six cycles (HR 0.335 [0.152–0.0.738]; p < 0.007) or more predicts greater OS compared to one to three cycles of immunotherapy. Low levels of serum albumin (HR 0.917 [0.852–0.987]; p = 0.021) and elevated levels of serum LDH (HR 2.224 [1.469–3.367]; p < 0.001) are associated with a reduced OS. Conclusion: The effectiveness of immunotherapy in fragile hospitalized patients is compromised, although they exhibit significant irAE. Excellent performance status, fitness to receive many IO treatments, and normal levels of serum LDH and albumin may be useful in selecting patients who will benefit from immunotherapy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".