Prealbumin to fibrinogen ratio is closely associated with diabetic peripheral neuropathy
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
The aim of our study was to explore the diagnostic value of prealbumin to fibrinogen ratio (PFR) for predicting prognosis with the optimal cut-off value in diabetic peripheral neuropathy (DPN) patients. A total of 568 type 2 diabetes mellitus (T2DM) patients were enrolled in this study. The values including Toronto clinical neuropathy score (TCNS), nerve conduction velocity (NCV), vibration perception threshold (VPT), blood cells count, biochemical parameters, fibrinogen and PFR were recorded. The patients were divided into tertiles based on admission PFR value. First, clinical parameters were compared among the groups. Secondly, a logistic regression and ROC analysis were performed as the statistical model. The percentage of DPN, TCNS and VPT were significantly higher in the lowest PFR tertile than in the middle PFR tertile and the highest PFR tertile (P < 0.01-0.001). NCV was significantly lower in lowest PFR tertile than in the middle PFR tertile and the highest PFR tertile (P < 0.01-0.001). The Spearman correlation analysis showed that PFR was negatively correlated with TCNS and VPT (P < 0.001), while PFR was positively correlated with median motor NCV (P < 0.001), peroneal motor NCV (P < 0.001), median sensory NCV (P < 0.001), and peroneal sensory NCV (P < 0.001). After adjusting these potentially related factors, PFR was independently related to DPN (P = 0.007). The area under ROC curve was 0.627. This study finds the first evidence to suggest PFR may be the key component associated with DPN in T2DM, while PFR might underlie the pathophysiologic features of DPN.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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