Patient-Reported Outcome Measures in Cancer Care
Bibliographic record
Abstract
Importance: Patient-reported outcome measures (PROMs) come directly from the patient, without clinician interpretation, to provide a patient-centered perspective. Objective: To understand the association of PROM integration into cancer care with patient-related, therapy-related, and health care utilization outcomes. Data Sources: Searches included MEDLINE and MEDLINE Epub ahead of print, in-process, and other nonindexed citations; Embase databases (OvidSP); PsychINFO; CENTRAL; and CINAHL from January 1, 2012 to September 26, 2022. Study Selection: Randomized clinical trials (RCTs) that enrolled adult patients (ages 18 years and older) with active cancer receiving anticancer therapy using a PROM as an intervention. Data Extraction and Synthesis: Pairs of review authors, using prepiloted forms, independently extracted trial characteristics, disease characteristics, and intervention details. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline was followed. Random-effects analyses were conducted. Main Outcomes and Measures: Overall mortality, health-related quality of life (HRQoL) measures, and hospital utilization outcomes. Results: From 1996 to 2022, 45 RCTs including 13 661 participants addressed the association of PROMs with outcomes considered important to patients. The addition of a PROM likely reduced the risk of overall mortality (HR, 0.84; 95% CI, 0.72-0.98; moderate certainty), improved HRQoL (range 0-100) at 12 weeks (mean difference [MD], 2.45; 95% CI, 0.42-4.48; moderate certainty). Improvements of HRQoL at 24 weeks were not significant (MD, 1.87; 95% CI, -1.21 to 4.96; low certainty). There was no association between the addition of a PROM and HRQoL at 48 weeks. The addition of a PROM was not associated with reduced ED visits (OR, 0.74; 95% CI, 0.54-1.02; low certainty) or hospital admissions (OR, 0.86; 95% CI, 0.73-1.02; low certainty). Conclusion and Relevance: The findings of this study suggest that the integration of PROMs into cancer care may improve overall survival and quality of life.
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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 | 0.156 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".