A Nutrition Referral Priority Rating System: In an Outpatient Oncology Centre
Bibliographic record
Abstract
PURPOSE: An oncology nutrition referral priority rating system (NRPRS) was developed and evaluated as a tool to classify patients into nutrition risk categories and allocate reasonable wait times. METHODS: A retrospective chart audit (n=112), patient focus groups (n=14), and a prospective chart audit (n=179) were conducted to refine the tool. Using the NRPRS, the dietitians assigned a priority rating from the information on the referral and then compared it with a second rating after the first visit. Education to referring staff was provided to improve the completeness of referrals. RESULTS: Patients rated at highest nutrition risk (priorities 1 and 2 [P1 and P2]) had a rating similar to the dietitian's after the first visit (P1, 97%; P2, 84%). Incomplete referrals were assigned a P3 rating. This may explain the discrepancy in ratings for P3 referrals (64%). After education, essential information on the referral form increased by 26%. CONCLUSIONS: The NRPRS is an effective tool for prioritizing high-risk patients when referrals are completed fully. The next step is to validate the NRPRS now that computerized order entry is implemented in the cancer clinic.
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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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".