Symptoms, Symptom Profiles, and Healthcare Utilization in Patients with Hematologic Malignancies: A Retrospective Observational Cohort Study and Latent Class Analysis
Bibliographic record
Abstract
Symptom burden is known to be high in patients with hematologic malignancies and can adversely impact patients' quality of life. The aims of this retrospective observational cohort study were to explore symptoms in patients with hematologic malignancies, including during the last year of life, to explore symptom profiles in patients with hematologic malignancies, and to explore associations among symptoms/symptom profiles and demographic, clinical, and treatment-related variables. Symptom prevalence and severity and symptom profiles were explored in patients with hematologic malignancies who completed patient-reported outcome measures (n = 6136) between October 2019 and April 2020. Emergency department visits and hospital admissions during the study period were reviewed. Chart audits were undertaken for patients who died within a year of completing patient-reported outcome measures (n = 432) to explore symptoms and healthcare utilization in the last year of life. Patients with hematologic malignancies in this study reported multiple symptoms co-occurring, with more than 50% of patients reporting four or more symptoms. Classes of co-occurring symptoms (symptom profiles) were associated with demographic and clinical factors as well as with healthcare utilization, particularly emergency department visits. The most reported symptoms were tiredness, impaired well-being, and drowsiness. The findings emphasize the need for more supports for patients with hematologic malignancies, particularly for symptom management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".