Retrospective Analysis of the Impact of a Dietitian and the Canadian Nutrition Screening Tool in a Geriatric Oncology Clinic
Bibliographic record
Abstract
Introduction: Canada’s aging population is leading to an increased number of older adults being diagnosed with cancer. This population faces unique challenges, including frailty, comorbidities, polypharmacy, and malnutrition, which can negatively affect treatment outcomes. The role of registered dietitians (RDs) in managing nutrition-related issues in this population is well-documented, but there is limited research on their integration into geriatric oncology clinics. We evaluated the impact of integrating a registered dietitian (RD) into the Older Adult with Cancer Clinic (OACC) at the Princess Margaret Cancer Centre, Toronto, Canada. Materials and Methods: A retrospective chart review was conducted of older adult cancer patients seen at the OACC, comparing outcomes before and after the RD’s integration. The focus was on weight characteristics and change, malnutrition screening/identification, and management. The two-item Canadian Nutrition Screening Tool (CNST) was introduced during the RD’s integration and was also examined to see its usefulness in identifying malnutrition risk. Chi-squared tests and t-tests were used for data analysis. Results: The pre-cohort (n = 140) had a mean age of 80.2 years, 48.6% female, and 77.9% vulnerable (Vulnerable Elders Survey (VES-13) ≥ 3). The post-cohort (n = 117) had a mean age of 81.4 years, 59.8% female, and 80.3% vulnerable (VES-13 ≥ 3). Weight change within 3 ± 1 months after the initial OACC consult was similar between pre and post groups with −1.4 kg and −1.2 kg, respectively (p = 0.77). Patients at nutritional risk, as determined by the OACC team, generated significantly more referrals to the RD in the post group (100% vs. 36.4%, p < 0.001). Among patients who had CNST screening and saw the RD, there was a higher rate of high nutrition risk among CNST-positive compared to CNST-negative patients (67.2% versus 44.4%, respectively). After the integration of the RD, a greater number of patients at nutritional risk received nutritional education and referrals to other healthcare professionals (43 versus 1). Conclusions: The integration of an RD into the OACC led to improved referral rates, nutritional education, and referrals to other healthcare professionals. Moreover, patients who were CNST positive were more likely to have high nutritional risk.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| 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".