Late Dysphagia Following Radiotherapy After Nasopharyngeal Carcinoma: A Case Series
Bibliographic record
Abstract
Purpose Standard treatment for nasopharyngeal carcinoma (NPC) is radiation therapy (RT); however, long-term effects of RT frequently include significant swallowing impairments (dysphagia; Gaziano, 2002 ; Hui, Chan, & Le, 2018 ). Our objective was to describe swallowing physiology in consecutive outpatients with a history of NPC following RT using standardized methods. Understanding dysphagia characteristics in this patient population could ultimately inform rehabilitation strategies and improve patient outcomes. Method We conducted a retrospective, observational, descriptive study of consecutive outpatients undergoing videofluoroscopic swallowing (VFS) exams at our clinic, from 2009 to 2014. We included those with a diagnosis of NPC treated with RT. Those with other cancer diagnoses; previous tracheostomy; acute neurological injury; and progressive, degenerative neurological conditions were excluded. Two registered MBSImP clinicians, blinded to each other, reviewed and scored the VFS exams according to previously published methods ( Martin-Harris et al., 2008 ). Following unblinding, a single reviewer collected demographic data from the electronic medical record. We reported overall impairment and MBSImP component scores descriptively. Results Of 158 outpatients undergoing VFS, 6 ( N ) met our inclusion criteria. The median time from completion of RT to outpatient VFS was 21.0 years. Patients reported a variety of dysphagia symptoms. All patients had high oral and pharyngeal residue scores (scores ≥ 2) and high impairment scores on components contributing to bolus transport and airway closure. Conclusions All patients presented with impairments in oral–pharyngeal bolus transport and airway protection. Our results identify specific swallowing impairments for this patient group highlighting possible latent RT effects on swallowing. This population would benefit from dysphagia rehabilitation and maintenance programs informed by multimodal diagnostic approaches.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".