Validation of the instrument "Infant Malnutrition and Feeding Checklist for Congenital Heart Disease", a tool to identify risk of malnutrition and feeding difficulties in infants with congenital heart disease
Bibliographic record
Abstract
Introduction: Introduction: currently, various tools have been designed to timely detect the risk of malnutrition in hospitalized children. In those with a diagnosis of congenital heart disease (CHD), there is only one tool developed in Canada: Infant Malnutrition and Feeding Checklist for Congenital Heart Disease (IMFC:CHD), which was designed in English. Objective: to evaluate the validity and reliability of the Spanish adaptation of the IMFC:CHD tool in infants with CHD. Methods: cross-sectional validation study carried out in two stages. The first, of translation and cross-cultural adaptation of the tool, and the second, of validation of the new translated tool, where evidence of reliability and validity were obtained. Results: in the first stage, the tool was translated and adapted to the Spanish language; for the second stage, 24 infants diagnosed with CHD were included. The concurrent criterion validity between the screening tool and the anthropometric evaluation was evaluated, obtaining a substantial agreement (κ = 0.660, 95 % CI: 0.36-0.95) and for the predictive criterion validity, which was compared with the days of hospital stay, moderate agreement was obtained (κ = 0.489, 95 % CI: 0.1-0.8). The reliability of the tool was evaluated through external consistency, measuring the inter-observer agreement, obtaining a substantial agreement (κ = 0.789, 95 % CI: 0.5-0.9), and the reproducibility of the tool showed an almost perfect agreement (κ = 1, CI 95 %: 0.9-1.0). Conclusions: the IMFC:CHD tool showed adequate validity and reliability, and could be considered as a useful resource for the identification of severe malnutrition.
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.
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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".