A New Simple Screening Tool—4QT: Can It Identify Those with Swallowing Problems? A Pilot Study
Bibliographic record
Abstract
As people and the population age, the prevalence of swallowing problems (dysphagia) increases. The screening for dysphagia is considered good practice in stroke care, yet is not routinely undertaken in the management of frail older adults. A short swallow screen, the 4QT, was developed following a review of the literature. The screen has four questions relating to swallowing that can be asked by a member of the health care team. A convenience sample of 48 older frail patients on an acute frailty ward was recruited into a Quality Improvement project. Their swallow was screened using the EAT-10 and 4QT. A speech and language therapist assessed for the presence of dysphagia using a standardised assessment for dysphagia. The 4QT was as effective as the EAT-10 in identifying older frail adults with potential swallowing problems (Κ = 0.73). The 4QT has 100% sensitivity, 80.4% specificity and positive predictive value (PPV) 50%, negative predictive value (NPV) 100%. The 4QT is a highly sensitive but not specific swallow screen, only 50% of people reporting swallowing problems were confirmed to have a degree of dysphagia by the SLT. The 4QT is a simple screening tool that could be used by all staff, but requires further research/evaluation before it is widely accepted into clinical practice.
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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 | 0.010 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".