Early nutrition intervention in cancer patients: A systematic review and meta-analysis.
Bibliographic record
Abstract
e14037 Background: Malnutrition is a common finding seen in 50-80% of cancer patients. Malnutrition negatively impacts quality of life (QOL), treatment-related outcomes, and survival. While early nutrition interventions (ENI) may be beneficial in preventing malnutrition, they are not part of routine cancer care. We conducted a systematic review and meta-analysis to assess the benefits of ENI compared to standard of care (SC) on nutritional status, QOL, and survival in patients with newly diagnosed cancer. Methods: We searched MEDLINE, EMBASE, Cochrane, and CINAHL databases from inception through October 2019 to identify randomized control trials (RCT) and cohort studies comparing ENI to SC in adult patients with newly diagnosed oncologic malignancies. We required that nutrition interventions began within 8 weeks of diagnosis and lasted at least 6 weeks. Outcomes of interest included nutritional status (change in weight or BMI), mortality, and QOL. We assessed for risk of bias among included studies using the Cochrane Risk of Bias tool (RCT) and the Newcastle-Ottawa scale (cohort studies). We summarized change in weight (kilogram, kg) at 3 and 6 months using standardized mean differences (SMD) and 95% Confidence Intervals (CI). Because some studies had insufficient data on weight to allow for quantitative analysis, we summarized their findings qualitatively. We used a qualitative approach to summarize QOL. Mortality at 1 and 2 years was compared using relative risk (RR) and 95%CI. Random effects models were used to pool data as there was meaningful and statistically significant heterogeneity (I2 > 50%; p-value < 0.1). Results: 2,781 studies were identified and screened by two independent reviewers for eligibility. 18 independent studies (subjects = 1936) met inclusion criteria. Among 9 studies included in the quantitative analysis for nutritional status, patients who received ENI had better nutritional status compared to SC (SMD at 3 months 0.49kg, 95%CI 0.08-0.79; at 6 months: 0.27kg, 95% CI 0.09-0.45). ENI was also associated with a lower risk of death compared to SC, although the findings were not significant (1 year RR: 0.41[95% CI 0.09-1.81]; 2 year RR: 0.79 [95 CI 0.58-1.09]). Of the ten studies reporting on QOL, six found that patients in the ENI group had improved QOL outcomes. There was insufficient data available to perform subgroup analysis based on cancer type. The quality of studies was generally good and 11 of the included studies had low risk of bias. Conclusions: Patients with newly diagnosed cancer may benefit from ENI due to better weight outcomes, QOL, and survival.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".