Assessing Impact of Nutrition Care by Registered Dietitian Nutritionists on Patient Medical and Treatment Outcomes in Outpatient Cancer Clinics: A Cohort Feasibility Study
Bibliographic record
Abstract
More information is needed about the impact of outpatient nutrition care from a registered dietitian nutritionist (RDN) on patient outcomes. This study aimed to assess the feasibility of a cohort study design to evaluate impact of RDN nutrition care on patient outcomes, describe clinic malnutrition screening practices, and estimate statistical parameters for a larger study. Seventy-seven patients with lung, esophageal, colon, rectal, or pancreatic cancer from six facilities were included (41 received RDN care and 36 did not). RDN nutrition care was prospectively documented for six months and documented emergency room visits, unplanned hospitalizations and treatment changes were retrospectively abstracted from medical records. Most facilities used the Malnutrition Screening Tool (MST) to determine malnutrition risk. Patients receiving RDN care had, on average, five, half hour visits and had more severe disease and higher initial malnutrition risk, although this varied across sites. Documented medical and treatment outcomes were relatively rare and similar between groups. Estimated sample size requirements varied from 113 to 5856, depending on tumor type and outcome, and intracluster correlation coefficients (ICCs) ranged from 0 to 0.47. Overall, the methods used in this study are feasible but an interventional or implementation design might be advantageous for a larger study.
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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 | 0.031 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".