The utility of abbreviated patient‐reported outcomes for predicting survival in early stage colorectal cancer
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcomes (PROs) are increasingly used in clinical settings. Prior research suggests that PROs collected at baseline may be associated with cancer survival, but most of those studies were conducted in patients with breast or lung cancer. The objective of this study was to determine the correlation between prospectively collected PROs and cancer-specific outcomes in patients with early stage colorectal cancer. METHODS: Patients who had newly diagnosed stage II or III colorectal cancer from 2009 to 2010 and had a consultation at the British Columbia Cancer Agency completed the brief Psychosocial Screen for Cancer (PSSCAN) questionnaire, which collects data on patients' perceived social supports, quality of life (QOL), anxiety and depression, and general health. PROs from the PSSCAN were linked with the Gastrointestinal Cancers Outcomes Database, which contains information on patient and tumor characteristics, treatment details, and cancer outcomes. Cox regression models were constructed for overall survival (OS), and Fine and Gray regression models were developed for disease-specific survival (DSS). RESULTS: In total, 692 patients were included. The median patient age was 67 years (range, 26-95 years), and the majority had colon cancer (61%), were diagnosed with stage III disease (54%), and received chemotherapy (58%). In general, patients felt well supported and reported good overall health and QOL. On multivariate analysis, increased fatigue was associated with worse OS (hazard ratio [HR], 1.99; P = .00007) and DSS (HR, 1.63; P = .03), as was lack of emotional support (OS: HR, 4.36; P = .0003; DSS: HR, 1.92; P = .02). CONCLUSIONS: Although most patients described good overall health and QOL and indicated that they were generally well supported, patients who experienced more pronounced fatigue or lacked emotional support had a higher likelihood of worse OS and DSS. These findings suggest that abbreviated PROs can inform and assist clinicians to identify patients who have a worse prognosis and may need more vigilant follow-up. Cancer 2017;123:1839-1847. © 2017 American Cancer Society.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".