Swallowing Assessment in Post-Comatose Patients: A Feasibility Study on the SWADOC Tool
Bibliographic record
Abstract
Background: After a severe brain injury and a coma, patients may develop disorders of consciousness (DoC), frequently accompanied by severe dysphagia. The evaluation and therapy of swallowing are therefore essential aspects of their management. Objectives: This study aims to evaluate the SWallowing Assessment in Disorders of Consciousness (SWADOC) tool in the assessment of swallowing in post-comatose patients. Here, we validate its quantitative items, describe preliminary results and identify limitations. Methods: Fourteen post-comatose patients were repeatedly evaluated with the Simplified Evaluation of CONsciousness Disorders (SECONDs) and with the SWADOC. Results: The internal consistency of the oral and pharyngeal subscales of the SWADOC was good. The test–retest reliability showed that all items, all subscores and the total score were stable except for two items (endo-buccal secretions and bronchial congestion). A comparison to the Facial Oral Tract Therapy Swallowing Assessment of Saliva (F.O.T.T-SAS) confirmed that scoring with the SWADOC offers a greater potential for quantitative observations in assessing swallowing abilities among patients with DoC. The SECONDs scores and SWADOC total scores showed a significant positive correlation (τ = 0.78, p < 0.001). Conclusions: This study provides preliminary but encouraging results on the psychometric properties of the SWADOC tool. It shows that this tool is relevant and feasible as a bedside assessment of dysphagia in patients with DoC.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".