Fähigkeit von Patienten mit einer peripheren Fazialisparese zur Erkennung von Emotionen – Eine Pilotstudie
Bibliographic record
Abstract
BACKGROUND: The perception of emotions is an important component in enabling human beings to social interaction in everyday life. Thus, the ability to recognize the emotions of the other one's mime is a key prerequisite for this. OBJECTIVE: The following study aimed at evaluating the ability of subjects with 'peripheral facial paresis' to perceive emotions in healthy individuals. METHODS: A pilot study was conducted in which 13 people with 'peripheral facial paresis' participated. This assessment included the 'Facially Expressed Emotion Labeling-Test' (FEEL-Test), the 'Facial-Laterality-Recognition Test' (FLR-Test) and the 'Toronto-Alexithymie-Scale 26' (TAS 26). The results were compared with data of healthy people from other studies. RESULTS: In contrast to healthy patients, the subjects with 'facial paresis' show more difficulties in recognizing basic emotions; however the results are not significant. The participants show a significant lower level of speed (right/left: p<0.001) concerning the perception of facial laterality compared to healthy people. With regard to the alexithymia, the tested group reveals significantly higher results (p<0.001) compared to the unimpaired people. CONCLUSIONS: The present pilot study does not prove any impact on this specific patient group's ability to recognize emotions and facial laterality. For future studies the research question should be verified in a larger sample size.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".