Utility Outcome Scores for Unilateral Facial Paralysis
Bibliographic record
Abstract
BACKGROUND: Facial paralysis is a debilitating condition. Dynamic and static facial reanimation remains a challenge for plastic surgeons and requires important resources. Our objective was to quantify the health state utility assessment (ie, utility score outcomes) of living with unilateral facial paralysis. METHODS: Utility assessments using visual analog scale, time trade-off, and standard gamble were used to obtain utility outcome scores for unilateral facial paralysis from a prospective sample of the general population and medical students. RESULTS: A total number of 123 individuals prospectively participated in the study. All measures (visual analog scale, time trade-off, and standard gamble) for unilateral facial paralysis [0.56±0.18, 0.78±0.21, and 0.79±0.21 respectively] were significantly different (P<0.0001) from the corresponding outcome scores for monocular blindness [0.61±0.21, 0.83±0.21, and 0.85±0.18, respectively] and binocular blindness [0.33±0.18, 0.65±0.28, and 0.65±0.29, respectively]. Linear regression analysis using age, race, income, and education as predictors of each of the utility scores for facial paralysis showed no statistical significance. CONCLUSIONS: In samples of the general population and medical students, all utility score outcome measures for facial paralysis were lower than those for monocular blindness. Our sample population, if faced with unilateral facial paralysis, would theoretically undergo facial reanimation procedures with a willingness to sacrifice 8 years of life and be willing to undergo a procedure with a 21% chance of mortality to attain perfect health, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".