The prevalence, nature and severity of oropharyngeal dysphagia in the acute post-operative phase following curative resection for esophageal cancer
Bibliographic record
Abstract
BACKGROUND: Oropharyngeal dysphagia and aspiration in the early post-esophagectomy period is rarely studied. This study investigated its prevalence, nature and severity, differences across surgical subgroups, and predictors of risk. METHODS: A prospective cohort study was conducted (January 2022-January 2024) at the National Esophageal Cancer Centre. Data was collected on post-operative day (POD) 4 or 5. Swallowing evaluations included videofluoroscopy [Dynamic Imaging Grade of Swallowing Toxicity v2(DIGESTv2), Modified Barium Swallow Impairment Profile (MBSImP), Penetration-Aspiration Scale (PAS)]. Functional Oral Intake Scale (FOIS) was used to identify oral intake status. RESULTS: N = 30 (25 males) were recruited, mean age (range) of 65 (46-80y), n = 13 2-stage, n = 8 3-stage, and n = 9 transhiatal resections. At POD 4/5, 60% (18/30) showed signs of aspiration, with no differences across surgical groups (P = 0.114). Dysphagia per the DIGESTv2 was present in 83% (25/30) of patients, with severe dysphagia in 23% (7/30). MBSImP assessment revealed reduced tongue base retraction (82%), pharyngeal residue (100%) and impaired neo-esophageal clearance (100%). Predictors of aspiration were: pre-operative abnormal FOIS (score < 7) (OR = 7.00, 95%CI 1.2-38.4; P = 0.024), and > 65 years (OR = 7.80, 95%CI 1.47-41.6; P = 0.016). Predictors for oropharyngeal dysphagia were: abnormal pre-operative FOIS (score < 7) (OR = 7.42, 95%CI 1.22-45.45; P = 0.029); age > 65 years (OR = 11.00, 95%CI 1.99-58.8; P = 0.006) and neoadjuvant treatment (OR = 7.20, 95%CI 1.08-47.96, P = 0.041). CONCLUSION: Oropharyngeal dysphagia and aspiration are prevalent in the early period after esophageal cancer surgery. These data should inform an increased input from speech and language specialists in the assessment and management of post-operative patients, and overall caution in the implementation and progression of early per orum intake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".