Obstetric anal sphincter injury
Bibliographic record
Abstract
Abstract Objective To investigate the association between disease progression and OCTA vessel density and other indices in patients with diabetic retinopathy. Methods Participants were selected with the following criteria: 63 patients (100 eyes) diagnosed with type 2 diabetes mellitus, which included 44 patients (72 eyes) with diabetic retinopathy and 19 patients (28 eyes) with type 2 diabetes mellitus and non-diabetic retinopathy (NDR), who were seen at the Eye Hospital China Academy of Chinese Medical Sciences from September 2020 to July 2021. All patients underwent OCTA examination, and FAZ, PERIM, AI, FD, SVD, DVD and other indices were counted. Results (1) The correlation coefficients of SVD, paracentric SVD, DVD, paracentric DVD and DR processes were: -0.525, -0.586, -0.323, and -0.424 (P< 0.05), respectively, and all were moderately negatively correlated. (2) The correlation coefficients of FAZ and PERIM with DR process were: -0.031, 0.084 (P>0.05), respectively, and not correlated. The correlation coefficients of AI and FD with DR process were: 0.307, −0.459 (P<0.05), and with moderate positive and negative correlations, respectively. (3) The correlation coefficients of FAZ, PERIM, AI and FD with age were: -0.124, -0.052, 0.113, -0.170 (P>0.05), and no correlation, respectively. Conclusion The disease progression of DR was moderately correlated with OCTA superficial vessel density and deep vessel density; and moderately correlated with AI and FD. OCTA could assist in the assessment of DR disease progression.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".