Predicting acute care use following the initiation of systemic therapy for solid tumors.
Bibliographic record
Abstract
6 Background: Emergency department (ED) visits and hospitalizations are undesirable and costly. We developed and validated the PROACCT (PRediction Of Acute Care use during Cancer Treatment) score to predict at least one acute care visit during the first 30 days (AC30) after initiating systemic therapy for cancer. Methods: Using administrative data, we identified patients in Ontario with the 18 most common solid tumors who started systemic treatment from July 1, 2014 to June 30, 2015. Southwestern and Northeastern Ontario formed the development and validation cohorts, respectively. We created a score to predict AC30 using multivariate logistic regression in the development cohort. Combinations of tumor sites and regimens were grouped into quintiles based on AC30. The score was assessed in the validation cohort. Results: AC30 occurred in 23% (4438/19359) of patients. Eleven factors predicted AC30 and formed the score: tumor site and regimen (2nd quintile: 2; 3rd-4th: 3; 5th: 4), recent ED visit (2), recent palliative radiation (1), rural residence (1) and Edmonton Symptom Assessment Scale anxiety (4+: 1), lack of appetite (4+: 1), pain (4+: 1), and wellbeing (4+: 1). Among the 204 tumor-regimen combinations, tumors with poor prognoses, such as pancreatic and lung, and platinum- and taxane-containing regimens, carried the highest risk for AC30. The score had moderate discrimination (c-statistic 0.65; P< 0.001) and strong calibration (Table) in the validation cohort. Conclusions: PROACCT identifies factors that predict AC30 in patients starting systemic treatment for solid tumors and could be incorporated into electronic health records to select patients for preventative interventions. [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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".