Using Patient Completed Screening Tools to Predict Risk of Malnutrition in Patients With Inflammatory Bowel Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background Malnutrition is associated with adverse clinical outcomes in patients with inflammatory bowel disease (IBD), however, malnutrition screening is not routinely performed. This study aimed to identify the prevalence of malnutrition in patients with IBD and compare the accuracy of patient completed screens to a gold-standard malnutrition assessment tool: the dietitian-completed subjective global assessment (SGA). Methods This cross-sectional study was conducted at 2 hospitals and 2 ambulatory care clinics in Alberta, Canada. Patients with IBD completed 4 malnutrition screening tools: abridged patient-generated SGA (abPG-SGA), Malnutrition Universal Screening Tool (MUST), Canadian Nutrition Screening Tool (CNST), and Saskatchewan IBD–nutrition risk (SaskIBD-NR). Risk of malnutrition was calculated for each tool and differences were compared between IBD subtype and body mass index (BMI) categories. Sensitivity and specificity, negative and positive predictive values (NPV and PPV), and area under the receiver operating characteristic curve (AUC) were calculated compared to SGA. Results Patients with Crohn’s disease (n = 149) and ulcerative colitis (n = 96) participated in this study. Overall prevalence of malnutrition using SGA was 23% and malnutrition risk for CNST, abPG-SGA, SaskIBD-NR, and MUST was 37%, 36%, 36%, and 27%, respectively. Overall, the abPG-SGA had the highest sensitivity (83%), PPV (53%), and NPV (94%), and largest AUC (0.837) compared to SGA. For patients with a BMI ≥25 kg/m2, sensitivity and PPV of the abPG-SGA decreased to 73% and 41%, respectively, with a AUC of 0.841. Conclusions Malnutrition is prevalent in patients with IBD and using malnutrition risk screening tools such as the abPG-SGA may be useful to identify patients who would benefit from further assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it