A Prognostic Symptom Model Incorporating Patient-Reported Symptoms for Transplant-Ineligible Patients with Multiple Myeloma
Bibliographic record
Abstract
INTRODUCTION: Patients with transplant-ineligible (TIE) multiple myeloma (MM) have high rates of symptom burden. The aim of this study was to develop and validate a prognostic model to predict symptoms in patients with TIE MM. METHODS: In this population-based, retrospective cohort study, using multiple administrative health care databases linked using a unique encrypted patient identifier in Ontario, Canada, symptoms were identified using the patient self-reported Edmonton Symptom Assessment System (ESAS) at each clinic visit. The primary outcome was the presence of moderate-to-severe (ESAS score 4-10) symptoms (specifically symptoms of pain, tiredness, depression, and impaired well-being) within one year from the index date. Using the entire cohort, a multivariable logistic regression model with baseline covariates was developed to predict the risk of experiencing each of the above symptoms, categorized as moderate to severe within 1 year post-index date. Internal validation of the model was assessed via bootstrap validation methods. RESULTS: A total of 1535 TIE adults with MM met the inclusion criteria. The median age was 75, with 25.2% of patients aged 80 years or older. In the multivariate analysis, baseline symptoms continued to be most associated with future symptom burden. Baseline severe pain (OR 9.84, 95% CI 6.29-15.7) was most associated with patients experiencing moderate-severe pain one year post-index date. Similarly, baseline severe tiredness (OR 17.34, 95% CI 9.00-33.42), baseline severe depression (OR 28.07, 95% CI 15.96-49.38), and baseline severely impaired well-being (OR 4.12, 95% CI 2.30-7.37) were the biggest predictors of patients experiencing moderate-severe tiredness, depression, and impaired well-being, respectively, at one year after the index date. CONCLUSIONS: Patients with MM experience persisting symptoms of pain, tiredness, depression, and impaired well-being, with baseline symptoms being the biggest predictor of future symptom burden.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".