Symptom burden and hospital length of stay among patients with curable cancer.
Bibliographic record
Abstract
6579 Background: Prolonged hospital admissions are often inconsistent with patients’ preferences and incur significant costs. While patients’ symptoms may result in hospitalizations, the relationship between patients’ symptom burden and their hospital length-of-stay (LOS) has not been fully explored in patients with curable cancers. Methods: We prospectively enrolled patients with curable cancer and unplanned hospital admissions between 8/2015 and 12/2016. Within the first 5 days of admission, we assessed patients’ physical (Edmonton Symptom Assessment System [ESAS]; scored 0-10 with higher scores indicating greater symptom burden) and psychological symptoms (Patient Health Questionnaire 4 [PHQ-4]; scored categorically and continuous with higher scores indicating greater distress). We created summated ESAS total and physical symptom variables. To assess the relationship between patients’ symptom burden and their hospital LOS, we used separate linear regression models adjusted for age, sex, marital status, education level, time since cancer diagnosis, and cancer type. Results: We enrolled 452 of 497 (91%) approached patients (mean age = 61.9 years; 188 [42%] female). Over half had hematologic cancers (n = 249, 55%). Mean hospital LOS was 8.3 days. Over one-tenth of patients screened positive for PHQ-4 depression (n = 74, 16%) and anxiety (n = 60, 13%) symptoms. Mean ESAS symptom scores were highest for fatigue (6.6), drowsiness (5.4), pain (4.9), and lack of appetite (4.8). In multivariable regression analysis, patients’ physical and psychological symptoms were associated with longer hospital LOS (table). Conclusions: Patients with curable cancer and unplanned hospital admissions experience a substantial symptom burden, which predicts for prolonged hospitalizations. Importantly, patients’ symptoms are modifiable risk factors that, if properly addressed, can improve care delivery and may have the potential to help decrease prolonged hospitalizations. [Table: see text]
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".