Validation of the <scp>IDDSI</scp> funnel for liquid flow testing
Bibliographic record
Abstract
In 2017, the International Dysphagia Diet Standardisation Initiative (IDDSI) introduced the IDDSI flow test which enables patients, clinicians, caregivers, food service professionals and researchers to classify liquid thickness into five levels based on the volume of liquid remaining in a standard 10 mL slip tip syringe after 10 s of flow under gravity. Within a few months of publishing the IDDSI flow test instructions, several barriers emerged: (1) the preferred model of syringe (BD 303134) was not equally accessible around the world, causing some users to perform flow tests with alternate models of syringe; (2) differences in syringe geometry across models led to variations in IDDSI flow test results; and (3) the need to use a second syringe for sample loading added complexity and cost to end users. To address these barriers, IDDSI designed the IDDSI funnel, a novel device, which combines the geometry of the BD 303134 syringe with a kitchen funnel to facilitate easy loading of liquid samples without need for a second syringe. In this report, we compare the IDDSI flow test results across two devices: syringe BD 303134 and IDDSI funnel. IDDSI level classifications were in complete agreement with the syringe reference test results in 67/73 (92%) of the test fluids and temperature conditions with mean difference of residual liquid across devices of 0.2 (2% full scale). These results demonstrate excellent correspondence between the two devices.
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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 | 0.031 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".