Comparing the M.D. Anderson Symptom and Dysphagia Inventories for Head and Neck Cancer Patients
Bibliographic record
Abstract
OBJECTIVES: Where patient-reported outcome measures (PROMs) may be administered at multiple patient visits, it is advantageous to capture these symptoms with as few questions as possible. In this study, the M.D. Anderson Head and Neck Symptom Inventory (MDASI-HN), and the M.D. Anderson Dysphagia Inventory (MDADI) is compared to determine if using the MDASI-HN alone would overlook symptoms identified with MDADI. METHODS: The MDASI-HN and the MDADI were completed by 156 patients, postradiotherapy for head and neck cancer (HNC). Associations between the two instruments were analyzed using correlation analysis, unsupervised machine learning, and sensitivity analysis. RESULTS: Little correlation was found between the two surveys; however, there was overlap between MDASI-HN dry mouth and many MDADI items, confirming that dry mouth is an important factor in difficulty swallowing, and patient QoL. Taking longer to eat (MDADI), was the most commonly reported item overall, with 85 (54%) patients rating it as moderate-severe. Dry mouth was the most endorsed MDASI-HN item (68, 44%). There were 51 patients missed by the MDASI-HN, reporting no moderate-severe symptoms, but reported one or more moderate-severe QoL impacts on MDADI. If patients who reported a score of 2 or higher on the MDASI-HN Dry Mouth item are flagged as requiring follow-up, the number of patients missed by MDASI-HN drops to 15. CONCLUSION: In an HNC clinic where MDASI-HN is routinely administered, assessment of symptoms and QoL might be enhanced by reducing the value at which MDASI Dry Mouth is considered moderate-severe to 2. LEVEL OF EVIDENCE: 3 Laryngoscope, 132:2388-2395, 2022.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".