Assessment of single screening questions for dysphagia for routine clinical use in a tertiary care practice.
Bibliographic record
Abstract
102 Background: Dysphagia is a common symptom among esophageal cancer patients. However, there is neither a validated PROMIS tool nor a validated PRO-CTCAE tool for this symptom. Clinically, obtaining patient-reported data on this symptom systematically in specific disease sites is important, but keeping the patient survey burden low means that this may be best done using a screening question first. The goal of this study was to assess the performance of such a screening question when compared to a more standard tool for measuring dysphagia, FACT-E, in a esophageal cancer outpatient clinic setting. Methods: Adult esophageal cancer patients seen at the Princess Margaret Cancer Centre between 2012-2014 were all seen by a multidisciplinary practice. Patients completed the FACT-E and screening symptom surveys at regular timepoints. The time point where the most severe set of symptoms was present for each patient was analyzed and severity of symptoms was compared against two 5-level screeners ("How are you currently eating?" (screener 1) and "I can swallow naturally and easily" (screener 2). Prevalence of symptoms, sensitivity (Se) and specificity (Sp) of the screeners was assessed. Complete data was available and analyzed in 173 (87%) of 199 patients. Results: Mean age (range) was 64 (23-89) and 75% were male. Prevalence of moderate to severe difficulty swallowing solid, soft, liquid foods and choking was 31%, 10%, 7%, 4% respectively. 34% of patients experienced at least one moderate to severe symptom of dysphagia. Screener 1 had a Se of 84% and Sp of 79% when using any moderate or severe swallowing problem as the reference. The Se and Sp of screener 2 was 51% and 87%, respectively. Conclusions: Up to a third of esophageal cancer patients experience moderate to severe symptoms of dysphagia. Screener 1 had the better performance as a screening tool. High completion rates in this practice suggest that routine systematic collection of such data is feasible, and perceived to be important to patients. Practice level projects can improve the quality of measuring symptoms. In the future we will be testing the role of mobile and computer technology for the routine collection of dysphagia and other symptoms in this practice setting.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".