A Markov multistate analysis of the relationship between performance status and death among an ambulatory population of cancer patients
Bibliographic record
Abstract
BACKGROUND: The relationship between performance status and death among cancer patients has been of increasing interest over the past years. However, few studies have implemented statistical models that adequately capture the longitudinal nature of performance status assessments collected under intermittent observation. AIM: The main research aims were to examine the association between performance status and death and to determine the probability of deterioration in performance status over time. DESIGN: This was a population-based longitudinal study among adult outpatients diagnosed with cancer. Throughout their observation period, all patients were repeatedly assessed for performance status using an 11-point scale with a score of 100 being the best, 10 being the worst and 0 representing death. A Markov multistate model accounting for intermittent observation was implemented in which each score represented a distinct state in the model. RESULTS: The cohort consisted of 27,739 patients with over 157,000 assessments. The rate of transition to death increases with a quadratic trend as performance status declines. The 1-month and 3-month probability of deterioration also increases with a quadratic trend as performance status declines. The relative rate of transition to death decreases as we compare lower scores (relative rate = 2.20 for comparing scores 90 vs 100 and relative rate = 1.23 for comparing scores 10 vs 20). CONCLUSION: There is a significant relationship between performance status and rate of transition to death. The Markov multistate model provides a comprehensive understanding of the shape of this relationship, which facilitates the interpretation of performance status and provides strength in its use as a prognostic tool in a clinical setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".