Retrospective study of dysphagia following hospital discharge of intensive care patients
Bibliographic record
Abstract
A retrospective study to assess the incidence and causal factors associated with long-term dysphagia following intensive care discharge. A questionnaire was sent out 4 months post ICU discharge to 193 intensive care patients (Level 3 care with a stay of over 48 hours). We reviewed the case notes of those patients who reported swallowing difficulties to establish whether they had undergone, had any characteristics of or received therapies potentially associated with dysphagia. We had a 50% response rate to our questionnaire. An overall dysphagia post ICU stay rate of 19.5% was observed. Fever and age over 65 were both common findings as one may expect and showed the highest association with subsequent dysphagia. We did not find any suggestion of a relationship between changing tracheostomy (suggesting repeat procedures) and subsequent difficulty swallowing. One patient within this group subsequently developed a tracheal stenosis. See Table 1 . We found the percentage of patients reporting swallowing difficulties post percutaneous tracheostomy (PCT) (Portex Blue Line Ultra tracheostomy tube) to be higher than one would expect. This may be confounded by neurological injury necessitating the need for a PCT, but we feel this may be an area of concern meriting further investigation given frequent PCT in ICU 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".