Patient-Reported Outcomes in Cancer Patients with HIV
Bibliographic record
Abstract
Elevated cancer-specific mortality in PWH has been demonstrated for non-AIDS-defining malignancies. However, additional clinical endpoints of interest, including patient-reported outcomes (PROs), have not been systematically examined in PWH and cancer. We evaluated differences in patient-reported symptomology between cancer patients with versus without HIV using data from 12,529 patients at the Moffitt Cancer Center, including 55 with HIV. The symptoms were assessed using the Edmonton Symptom Assessment Scale (ESAS), which asks patients to rank 12 symptoms on a scale of 1−10, with scores ≥7 considered severe. The responses across all questions were summed to create a composite score. Vital status through t July 2021 was determined through linkage to the electronic health record. PWH reported a higher composite ESAS score on average (44.4) compared to HIV-uninfected cancer patients (30.7, p-value < 0.01). In zero-inflated negative binomial regression models adjusted for cancer site, sex, and race, the composite ESAS scores and the count of severe symptoms were 1.41 times (95% CI: 1.13−1.77) and 1.45 times (95% CI: 1.09−1.93) higher, respectively, in cancer patients with HIV. Among PWH, higher ESAS scores were associated with mortality (p-value = 0.02). This is the first demonstration of uniquely poor PROs in PWH and cancer and suggests that patient symptom monitoring to improve clinical endpoints deserves further study.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".