Bibliographic record
Abstract
Nutrition is an important component of a patient’s overall health, and cases of malnutrition may be more prevalent than realized. This commentary presents a brief overview of an easy-to-use nutrition screening tool created during a Canadian study. In the recent Nutrition Care in Canadian Hospitals (NCCH) Study conducted by the Canadian Malnutrition Task Force, a 45% prevalence of malnutrition on admission was found among 1015 patients admitted to medical and surgical wards of 18 Canadian hospitals. The study was conducted from July 2010 to February 2013 in patients 18 years or older who were admitted to the hospital for more than 2 days. Excluded were those admitted directly to the intensive care unit; obstetric, psychiatry, or palliative units; or medical day units. Malnutrition was independently associated with prolonged length of stay (LOS).1 It is known that malnutrition is also related to detrimental outcomes such as delayed wound healing. Moreover, very few malnourished patients are identified on admission in order to provide prompt nutrition care.2 Nutrition screening remains the process for early identification of patients who are malnourished or at risk for malnutrition. In the hospital setting, nutrition screening should be conducted on admission by the frontline nursing staff.3 The NCCH study included a nursing survey that showed 91% of nurses agreed that 2 or 3 nutrition screening questions could be integrated into patient admission histories.4 An efficient nutrition screening process relies on a simple, valid, and reliable tool. In the NCCH study, the Canadian Nutrition Screening Tool (CNST) was developed (Figure). It initially included 2 questions about weight loss and decreased food intake and the body mass index (BMI) calculation. The first criterion validity and the predictive validity of this tool have been tested in the NCCH study. The Subjective Global Assessment (SGA) was the criterion standard, and the screening tool was completed by the researchers. This first validity assessment of the tool showed promising results: (1) sensitivity, 91.7% (correctly identifies patients at nutrition risk or who are malnourished), and specificity, 74.8% (correctly identifies patients who are not at nutrition risk or malnourished), which indicated good potential of the tool to screen, and (2) the tool could significantly predict clinical outcomes: LOS (P < .001), 30-day readmission (P = .02, odds ratio [OR] = 1.56; 95% confidence interval [CI], 1.07–2.27), and mortality (in hospital or within 30 days of discharge) (P < .001, OR = 5.37; 95% CI, 2.36–12.79).3Figure.: The Canadian Nutrition Screening ToolThe reliability and the second criterion validity of the CNST were assessed in a second study with 150 patients admitted to medical and surgical wards of 3 Canadian hospitals. In this study, the CNST was completed by untrained nursing personnel (n = 160) and 1 nutrition technician to better reflect the real-world hospital setting. To test the interrater reliability of the tool, the CNST was completed by 2 blinded, independent raters for each patient. Reliability results showed a κ coefficient of 0.88 (95% CI, 0.80–0.97), which indicates an almost perfect agreement between the raters. The SGA conducted by the research associates was used to measure the criterion validity of the tool. While using 2 “yes” answers for classifying the patient at nutrition risk, the CNST showed a sensitivity of 73% and a specificity of 86% (rater 1), which is considered adequate performance for a clinical tool. Interestingly, validity results were very similar with or without the inclusion of BMI in the tool. As a result, BMI was removed from the tool to promote ease of use, because calculating the BMI is likely challenging to busy hospital staff.3 Tackling malnutrition in Canadian hospitals requires an interprofessional approach where the first step is nutrition screening. The CNST is the first valid and reliable tool tested by untrained nursing personnel, which represents the reality of a hospital setting. The CNST (Figure) is a simple tool that poses 2 questions, and when the answer is “yes” for both questions, a patient is classified at nutrition risk and will require an evaluation by the dietitian. It is recommended that hospitals include the CNST in the nursing admission questionnaire and the electronic medical record for early recognition of malnourished patients. These steps will help facilitate the appropriate screening and referral process.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".