A prospective evaluation of symptom prevalence and overall symptom burden among cohort of critically ill cancer patients
Bibliographic record
Abstract
BACKGROUND: Gross physiological perturbations necessitating the Intensive Care Unit (ICU) admission might exacerbate the already existing or initiate bothersome symptoms among cancer patients. There is a lack of conclusive evidence concerning the symptomatic experience among this subgroup of cancer patients particularly so in the Indian population. The aim of this prospective observational study was to elucidate the symptom prevalence and overall symptomatic distress among critically ill cancer patients at the time of admission to a medical ICU. METHODS: We prospectively evaluated 110 consecutive cancer patients at the time of admission to our medical ICU for the presence and intensity of symptoms using a modified Edmonton Symptom Assessment Scale (ESAS). The patients/caregivers were also enquired regarding the most bothersome symptom in the past 1 week and the presence of "symptom associated sleep disturbance." The primary outcome was the prevalence of patients with moderate (ESAS ≥ 40) and severe (ESAS ≥ 70) symptomatic distress. RESULTS: The average age was 52.49 years with 75.45% of the respondents in the economically productive age group (21-60 years). Carcinoma breast (19.35%) and lung (14.58%) were the most common cancers among females and males, respectively. 87.27% and 60% of the patients had advanced cancer and multi-organ dysfunction, respectively. About 76.36% patients were able to complete ESAS either by themselves or with caregiver's assistance within first 24 h of ICU admission. The mean ESAS distress score was 48.04 (0-81) with 72.72% of the patients having moderate-severe symptomatic distress. Loss of appetite (92.73%) and nausea (54.55%) were the most common and the least common reported symptoms, respectively. Pain was the most common and "most distressing symptom" reported by 40% of patients with 64.55% patients reporting one or more symptoms severe enough to interfere with their sleep. CONCLUSION: ESAS is a user-friendly cognitive aid to make the healthcare team cognizant of the symptom existence and overall symptomatic burden among cancer patients with gross physiological perturbations. The high prevalence of moderate-severe symptom distress requires the concomitant provision of palliative and intensive care among this group of cancer patients.
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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 | 0.001 | 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".