Is IDDSI an Evidence-Based Framework? A Relevant Question for the Frail Older Population
Bibliographic record
Abstract
To delay impacts of aging, optimal nutritional status is essential. Several factors can reduce food intake, such as isolation, income, and cognitive/physical decline. Additionally, chewing and swallowing difficulties, or dysphagia, often disrupt the ability to consume life-long favorite dishes. Food and liquids could require modification of texture or consistency to ensure a comfortable or safe swallow. The food industry, foodservices facilities, and caregivers need quality control benchmarks to provide adequate nourishment and meet these new feeding challenges. The International Dysphagia Diet Standardisation Initiative (IDDSI) is proposing the IDDSI framework and testing methods to describe food used in nutritional care plans to circumvent dysphagia and improve communication among caregivers. This systematic review assesses the validity and reliability of the IDDSI testing methods using the Consensus-based Standards for the selection of health Measurement Instruments (COSMIN). Two publications presented content validity whereas 19 publications looked at construct validity or reliability for the IDDSI testing methods. One study was conducted in older adults presenting dysphagia. This review concludes that there is insufficient evidence to recommend the IDDSI testing methods. Further research, conducted with robust methodological design and reporting, is needed to develop and assess nutritious adapted food for frail older populations.
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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 | 0.076 | 0.235 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".