Diabetes Distress Among Patients Undergoing Surgery for Diabetic Retinopathy and Associated Factors: A Cross-Sectional Survey [Letter]
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A Cross-Sectional Survey" by Zhang et al. 1 This is a valuable observational study with the following advantages, namely: (1) This is a rare study investigating the prevalence of diabetes distress (DD) and its associated factors among patients undergoing surgery for diabetic retinopathy (DR) in China. Identifying the factors associated with DD among DR surgery patients and providing targeted psychological support and nursing care are the responsibility and mission of health care professionals. (2) The authors explicitly elaborated the basis for selecting the following predictive variables (eg, self-management, family support, social support, and partial demographic and disease-related factors) related to DD. (3) The authors used a comprehensive approach to conduct normality tests of the data, 2 such as kurtosis and skewness coefficients, histogram, Kolmogorov-Smirnov and Shapiro-Wilk tests. (4) The authors conducted an in-depth discussion regarding their research findings, fully compared them with previous studies performed in other countries (eg, USA, Canada, Vietnam, Bangladesh, Portugal, etc), and analyzed in detail the reasons for the differences between different results. In addition, the authors also provided some valuable recommendations for the disease management of DR surgery patients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it