Impact of proactive symptom monitoring on quality of life (QoL) and treatment toxicity in patients with cancer receiving chemotherapy: A meta-analysis of randomized clinical trials.
Bibliographic record
Abstract
1545 Background: Early recognition and management of symptoms can improve outcomes in cancer patients receiving treatment. A number of randomized trials have investigated the effects of adding proactive symptom monitoring to usual care (UC). These include web-based, application-based, or telephone-based assessments. Results have been variable, and the impact of proactive symptom monitoring on QoL, treatment toxicity and utilization of unscheduled acute care remains unclear. Methods: A systematic search of MEDLINE identified prospective, randomized trials that studied the effect of proactive monitoring and intervention versus UC in cancer patients receiving chemotherapy. The difference between proactive symptom monitoring and UC on the mean and SD for QoL using validated scales was collected for each study and pooled in a meta-analysis. Analysis was performed using the standardized mean difference (SMD) using random-effects modeling. The effect size was reported as the Hedges’ adjusted g. We also calculated the odds ratios (OR) for the occurrence of several common symptoms of any grade in the individual trials and pooled them in a meta-analysis. Statistical significance was defined as P < 0.05. Quantitative significance was defined as a difference in QoL score exceeding the minimal clinically important difference (MCID) based on previous studies for each QoL framework. Results: Of the 17 trials which met eligibility criteria, FACT-G and EORTC QLQ C30 were the most consistently utilized QoL tools. The mean difference in score between intervention and control at the last evaluation visits was 2.82 (95% CI -0.57 to 6.21; P = 0.10) in FACT-G and 2.33 (95% CI -0.29 to 4.96; P = 0.08) in EORTC QLQ C30, neither of which met quantitative or statistical significance. There was a statistically significant reduction in fatigue (OR 0.67, 95% CI 0.46 to 0.97; P = 0.04), but no difference in constipation (OR 0.63, 95% CI 0.34 to 1.17; P = 0.15), nausea (OR 1.03, 95% CI 0.72 to 1.47; P = 0.89), pain (OR 0.83, 95% CI 0.62 to 1.10; P = 0.19), or diarrhea (OR 1.41, 95% CI 0.40 to 5.01; P = 0.60). SMD for symptom severity was calculated for fatigue, diarrhea, and nausea. Severity of fatigue was statistically lower with proactive symptom monitoring (SMD -0.45, 95% CI -0.69 to -0.22; I² = 0%, P < 0.001), however, magnitude of effect was modest. Conclusions: Proactive symptom monitoring in cancer patients receiving treatment is not associated with significant or meaningful QoL improvement. Similarly, there is limited impact on individual toxicity.
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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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.062 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".