The Prevalence of Dyslipidemia in Patients Attending the Post Kidney Transplant Clinic at St Paul’s Hospital
Bibliographic record
Abstract
Purpose: To implement a nutrition risk screening tool for all inpatient rehab units at Toronto Rehab Institute (TRI) and to complete a process evaluation on its use. Process: A review of best practice indicates that systematic nutrition risk screening should be completed for all new admissions to ensure that no high-nutrition risk patients are missed (Mueller et al, 2011). An 'Identification of Nutrition Risk Level' screening tool was developed over 10 years ago at TRI in order to flag high risk patients to the dietitian. Due to inconsistent completion and accuracy, the need for a revised screening tool became evident. Systematic approach used: An updated nutrition risk screening tool was informed and guided by the TRI Clinical Best Practice Process, which included a needs assessment, review of present practice, literature review of best practice, and a gap analysis (McGlynn et al, 2010). The updated tool was adapted from the Canadian Nutrition Screening Tool and includes additional information regarding common reasons for dietitian intervention in rehab. Conclusions: Nursing education was provided for 93 inpatient rehab nurses. Completion rates for new admissions improved from an average of 48% of the time to 87%. The accuracy of information on the completed tools also improved, from 50-80% to 90%. Recommendations: Ongoing training and auditing is needed to sustain this change. Moreover, an outcome evaluation will be helpful to further understand the long-term impact of the tool on dietetic practice and patient care (Eglseer et al, 2019). Significance to the field of dietetics: There are currently no validated screening tools developed for the rehab patient population (Marshall et al, 2016). The positive results observed from the present screening tool may help bridge the gap regarding systematic nutrition risk screening among rehab patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".