The PROVIEW+ tool: Developing and validating a tool to predict risk of poor performance status and severe symptoms in cancer patients over time.
Bibliographic record
Abstract
12095 Background: There are numerous predictive cancer tools that focus on survival. However, no tools predict risk of low performance status or severe symptoms, which are important for patient decision-making and early integration of palliative care. The aim of this study was to develop and validate a model for all cancer types that predicts the risk for having low performance status and severe symptoms. Methods: A retrospective, population-based, predictive study using linked administrative data from cancer patients from 2008-2015 in Ontario, Canada. Patients were randomly selected for model derivation (60%) and validation (40%). The derivation cohort was used to develop a multivariable logistic regression model to predict the risk of having the reported outcomes in the subsequent 6 months. Model performance was assessed using discrimination and calibration plots. The main outcome was low performance status using the Palliative Performance Scale. Secondary outcomes included severe pain, dyspnea, well-being, and depression using the Edmonton Symptom Assessment System. Outcomes were recalculated after each of 4 annual survivor marks. Results: We identified 255,494 cancer patients (57% female; median age of 64; common cancers were breast (24%) and lung (13%)). At diagnosis, the risk of having low performance status, severe pain, well-being, dyspnea, and depression in 6-months is 1%, 3%, 6%, 13% and 4%, respectively for the reference case (i.e. male, lung cancer, stage I, no symptoms). Generally these covariates increased the outcome risk by > 10% across all models: obstructive lung disease, dementia, diabetes; radiation treatment; hospital admission; high pain; depression; Palliative Performance Scale score of 60-10; issues with appetite; or homecare. Model discrimination was high across all models. Conclusions: The model accurately predicted changing cancer risk for low performance status and severe symptoms over time. Providing accurate predictions of future performance status and symptom severity can support decision-making and earlier initiation of palliative care, even alongside disease modifying therapies.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".