Exploring Patient Experience of Facial Nerve Palsy to Inform the Development of a PROM
Bibliographic record
Abstract
BACKGROUND: There is currently a mandate globally to incorporate patient's perceptions of their illness into outcome measures, in order to provide a deeper insight into medical practice. Facial nerve palsy (FNP) is a devastating condition that can significantly impact quality of life. However, no measure currently exists that comprehensively assesses outcome in FNP using patient perception. The aim of this study is to explore patients' experiences of FNP with the aim of informing the development of a patient-reported outcome measure. METHODS: Presented is a qualitative study, using in-depth semi-structured interviews with FNP patients. An interview guide was developed using expert opinion and a literature review. Interpretative description was used as the qualitative approach. Interviews were audio-recorded, transcribed, and coded line-by-line. Codes were refined using the constant comparison approach. Interviews continued until data saturation was reached. The data were used to develop a conceptual framework of patient perceived issues relating to FNP. RESULTS: The sample included 5 men and 9 women aged 57.7 years (range, 36-78) with a range of causes of FNP, including Bell's palsy (n = 5), acoustic neuroma (n = 3), trauma (n = 2), meningioma (n = 1), muscular dystrophy (n = 1), congenital (n = 1), and Ramsay Hunt syndrome (n = 1). Analysis of the 14 participant interviews led to identification of 5 major domains including "facial function concerns," "appearance concerns," "psychological function," "social function," and "experience of care." CONCLUSION: This study provides a conceptual framework covering outcomes that matter to patients with FNP, which can be used to inform the development of a new comprehensive FNP-specific patient-reported outcome measure.
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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 | 0.047 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".