Trajectory of Performance Status and Symptom Scores for Patients With Cancer During the Last Six Months of Life
Bibliographic record
Abstract
PURPOSE: Ontario's cancer system is unique because it has implemented two standardized assessment tools population-wide to improve care: the Edmonton Symptom Assessment System (ESAS) measures severity of nine symptoms (scale 0 to 10; 10 indicates the worst) and the Palliative Performance Scale (PPS) measures performance status (scale 0 to 100; 0 indicates death). This article describes the trajectory of ESAS and PPS scores 6 months before death. PATIENTS AND METHODS: Observational cohort study of cancer decedents between 2007 and 2009. Decedents required ≥1 ESAS or PPS assessment in the 6 months before death for inclusion. Outcomes were the decedents' average ESAS and PPS scores per week before death. RESULTS: Ten thousand seven hundred fifty-two (ESAS) and 7,882 (PPS) decedents were included. The mean age was 65 years, half were female, and approximately 75% of assessments occurred in cancer clinics. Average PPS score declined slowly over the 6 months before death, starting at approximately 70 and ending at 40, declining more rapidly in the last month. For ESAS symptoms, average pain, nausea, anxiety, and depression scores remained relatively stable over the 6 months. Conversely, shortness of breath, drowsiness, well-being, lack of appetite, and tiredness increased in severity over time, particularly in the month before death. More than one third of the cohort reported moderate to severe scores (ie, 4 to 10) for most symptoms in the last month of life. CONCLUSION: In this large outpatient cancer population, trajectories of mean ESAS scores followed two patterns: increasing versus generally flat. The latter was perhaps due to available treatment (eg, prescriptions) for those symptoms. Future research should prioritize addressing symptoms that worsen over time.
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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 | 0.001 | 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".