The effects of priming on rhinologic patient reported outcome measures: a randomized controlled trial.
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures (PROMs) are questionnaires designed to assess a patient's perception of their medical condition. The 22-item Sino-Nasal Outcomes Test (SNOT-22), the Rhinosinusitis Disability Index (RSDI) and the mini-Rhinoconjunctivitis Quality of Life Questionnaire (MiniRQLQ) are validated PROMs commonly used to assess rhinologic conditions. The objective of this study is to determine if responses on these PROMs may be influenced by priming respondents with positive or negative health-related questionnaires. METHODS: Nine hundred patients were prospectively randomized to one of nine groups. Groups A, D and G were positively primed prior to completing the SNOT-22, the RSDI and MiniRQLQ, respectively. Groups B, E, and H were negatively primed. Groups C, F, and I served as control groups, completing the PROMs without priming. Priming was performed by administering a survey designed to make patients think about their health-related quality of life in a positive or negative way. RESULTS: Patients who were primed negatively had statistically significantly worse scores on the SNOT-22, RSDI and MiniRQLQ when compared to patients who were primed positively. When compared to the control group, patients who were primed negatively had statistically worse scores on the SNOT-22 and RSDI. There was no significant difference in scores between the positive priming and the control groups for any PROM. CONCLUSIONS: Priming subjects regarding their health-related quality of life impacts their responses on rhinologic PROMs. Further study is required to understand the clinical and research implications of this novel finding and to clarify the optimal manner for administering and interpreting PROMs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".