High Symptom Burden in Patients With Advanced Chronic or Prolonged Infectious Diseases: Not Only Pain
Bibliographic record
Abstract
INTRODUCTION: The growing evidence of increased life expectancy in the future reveals the high relevance of frailty in patients with chronic-degenerative diseases; identification and management of symptoms may improve significantly their quality of life. The objective of our study was to assess the symptom burden in patients with advanced chronic or prolonged infectious diseases. MATERIALS AND METHODS: A cross-sectional study was performed enrolling 88 patients, referred to palliative care consultation for chronic pain, and evaluated using the Edmonton Symptom Assessment System to define Total Symptom Distress Score (TSDS) and high symptom burden (HSB) when more than six symptoms along with Numerical Rating Scale ≥4 were present. RESULTS: All participants reported moderate to severe pain; in addition, 86 (97.7%) experienced a lack of well-being, 81 (92%) tiredness, 67 (76.1%) lack of appetite, 66 (75%) drowsiness, 66 (75%) depression, 56 (63.6%) anxiety, 49 (55.6%) nausea, and 39 (44.3%) shortness of breath. Forty-four patients (50%) had high TSDS, greater than 40.5, and presented lower Karnofsky Performance Scale (KPS) (median 40 vs. 70, p=0.0005), higher comorbidities (median 7 vs. 4, p=0.00001), and higher drug burden (median 9 vs. 6, p=0.0003) than those with low TSDS. Furthermore, considering symptom intensity, 40 patients (45.4%) had HSB and presented lower KPS (median 50 vs. 70, p=0.0005), higher comorbidities (median 7 vs. 4, p=0.00001), and higher drug burden (mean 9 vs. 6, p=0.01) compared to patients without HSB. CONCLUSION: Our population had an HSB, in addition to pain, revealing high frailty. A correct assessment of symptoms is, therefore, required to manage patients with chronic infectious diseases. In this setting, attention should be given to identifying patients at high risk of HSB through a correct diagnosis and effective management, which should be based on a multi-professional approach.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".