Label-free enrichment of human pluripotent stem cell-derived early retinal progenitor cells for cell-based regenerative therapies
Bibliographic record
Abstract
The Eating Attitude Test-26 (EAT-26) is a widely used screening tool to measure symptoms and behaviors related to eating disorders (EDs). Previous studies in various countries have empirically assessed the original factor structure of EAT-26 based on their respective sample data. Their sample data did not support the original factor structure of EAT-26. Sociocultural factors may cause ED behaviors to differ among diverse sociocultural groups. However, assessing the EAT-26 factor structure using confirmatory factor analysis (CFA) has not been done in Thailand. Thus, this cross-sectional study aimed to examine the factor structure of EAT-26 among 210 young adults in Northeast Thailand using both exploratory factor analysis (EFA) and CFA. The results of our study revealed similar findings to those of previous studies. The first finding indicated that the factor structure of the EAT-26 was inconsistent with the sample data obtained using EFA. Similarly, the second CFA finding indicated that the (EAT-26) model did not fit the sample data. However, the sample data from those previous studies and our sample data differed from those included in the original EAT-26 study. In other words, the sample data come from those participants who did not share the same sociocultural backgrounds different than those included in the original EAT-26 study. It can be concluded that sociocultural factors appear to play a crucial role in the ED screening stage. Therefore, adapting the ED screening tools to suit groups with different sociocultural backgrounds (e.g., young Thai adults) may be essential. Finally, future research should focus on developing an ED scale bank that accommodates the growing screening tools sensitive to sociocultural factors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".