Thematic Analysis of Canadian Patient-Reported Outcomes in Facial Nerve Paralysis: A Combined Interpretive Description and Modified Delphi Approach
Bibliographic record
Abstract
Background: In facial nerve dysfunction literature, subjective reporting tools lack essential construct validity arising from a patient-driven design process. Objective: Elicit patient-identified themes of importance pertaining to disease course in facial nerve dysfunction from a variety of etiologies. Methods: Twenty participant interviews were conducted from a standardized script and analyzed using a thematic analysis framework. Subsequently, four participants participated in a modified Delphi focus group for consensus of relative theme and domain importance. Results: Upon thematic analysis of 315 codable phrases, 33 codes were sorted into six domains. In descending order: smiling, facial symmetry, surgical access, self-consciousness, eye care, eating, lip movement, eye closure, beverage consumption, speech, chewing, drooling, eyebrow raise, mouth closure, and ptotic vision limitations were identified as the most important aspects of disease course. Care experience, defined as areas of interaction with the health care system in which patients felt strongly about their care or outcome, was the most important domain to participants. Conclusion: Patients with facial nerve dysfunction identified care experience as the highest domain of importance, and value smiling, facial symmetry, and access to surgical treatments.
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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 | 0.109 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".