Development and evaluation of screening dysphagia tools for observational studies and routine care in cancer patients
Bibliographic record
Abstract
BACKGROUND AND AIMS: Dysphagia can be associated with significant morbidity in cancer patients. We aimed to develop and evaluate dysphagia screener tools for use in observational studies (phase 1) and for routine symptom monitoring in clinical care (phase 2). METHODS: Various dysphagia or odynophagia screening questions, selected after an expert panel reviewed the content, criterion, and construct validity, were compared with either functional assessment of cancer therapy - esophageal cancer (FACT-E) Swallowing Index Cut-Off Values or to questions adapted from the Patient Reported Outcomes for Common Terminology Criteria for Adverse Events. Sensitivity, specificity, and patient acceptability were assessed. RESULTS: In Phase 1 (n = 178 esophageal cancer patients), the screening question "How are you currently eating?" had the highest sensitivities and specificities against various Swallowing Index Cut-Off Value cut-offs, with the best optimal cutoff associated with weight loss (80% sensitivity and 75% specificity). In phase 2 (255 head and neck, gastro-esophageal, and thoracic cancer patients), a single question screener ("Do you experience any difficulty or pain upon swallowing?") versus a Patient Reported Outcomes for Common Terminology Criteria for Adverse Events-like gold standard generated sensitivities between 86% and 94% and specificities between 93% and 100%. This screening question (+/- follow-up questions) had a median completion time of under 2 minutes, and >90% of patients were willing to complete the survey electronically, did not feel that survey made clinic visit more difficult, and did not find the questions upsetting or distressful. CONCLUSION: Our results demonstrate that these screener tools ("How are you currently eating?", "Do you experience any difficulty or pain upon swallowing?") can effectively screen dysphagia symptoms without increasing cancer outpatient clinic burden, both in observational studies and for routine clinical monitoring.
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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 | 0.217 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".