Screening for cancer-associated dysphagia: The development of two rapid tools for use in observational studies and routine care.
Bibliographic record
Abstract
164 Background: Dysphagia as a cancer symptom can be associated with significant morbidity. We developed dysphagia screener tools for use in observational studies (Phase 1) and routine symptom monitoring in clinical care (Phase 2). Methods: Various dysphagia or odynophagia screening questions, selected after an expert panel reviewed content, criterion, and construct validity, were compared to either FACT-E Swallowing Index Cut-Off Values (SICV) or to questions adapted from the Patient Reported Outcomes for Common Terminology Criteria for Adverse Events (PRO-CTCAE). Sensitivity, specificity and patient acceptability were assessed. Results: In developing a tool for observational studies (Phase 1; n = 178 esophageal cancer patients), the screening question, “How are you currently eating?” had the highest sensitivities and specificities against various SICV cut-offs, with the best optimal cut-off associated with the clinical outcome of weight loss (80% sensitivity, 75% specificity). When developing a rapid screening tool for routine symptom monitoring (Phase 2; 255 head and neck, gastro-esophageal, and patients undergoing thoracic radiation), a single question screener (“Do you experience any difficulty or pain upon swallowing?”) versus a PRO-CTCAE-like gold standard generated sensitivities between 86-94% and specificities between 93-100%. The screening question (+/- follow-up questions where indicated) had a median completion time of under 2 minutes, and > 90% of patients were happy to complete the survey on an electronic tablet, did not feel that survey completion made their clinic visit more difficult, and did not find the questions upsetting or distressful. Conclusions: Two screener tools (for prospective observational studies “How are you currently eating?”, and for routine clinical monitoring “Do you experience any difficulty or pain upon swallowing?”) can effectively screen dysphagia symptoms without increasing cancer outpatient clinic burden.
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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 | 0.365 | 0.446 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".