Bibliographic record
Abstract
Sir, Thank you for the response.[1] A) As has been pointed out in the ‘Introduction’ section, our study includes a clinically important outcome like neuropathyin addition to B12 deficiency as a response variable. The effect of metformin on the neuropathy scores has been evaluated by only two studies previously to the best of our our knowledge.[2,3] Furthermore, this is Indian study has evaluated the effect of metformin on neuropathy and B12 scores in a very large cohort. Our aim was to examine the association between metformin use and these outcome variables. The causation has been addressed by two large placebo controlled studies.[4,5] B) Both groups were matched for age and duration of diabetes. We also accounted for the potential confounding caused by these variables by conducting multiple linear regression with B12 levels and the Toronto clinical scoring system (TCSS) score as dependent variables, and age, duration of diabetes, HbA1c, and the cumulative metformin dose as independent variables. The multiple linear regression helped us to account for the effect of these potentially confounding variables. In our study, the cumulative metformin dose and HBA1c emerged as independent explanatory variables for neuropathy after controlling for age and duration of diabetes, (refer methods(F ratio = 23.39, R square = 0.45, P < 0.001). This also showed that HbA1c was an independent risk factor for neuropathy and a higher HbA1c (HbA1C 8.2 ± 1.02 vs. 8.4 ± 0.81) in both groups accounted for the 21 extra cases of neuropathy (n = 45) not explained by B12 deficiency alone, (n = 24). We attempted to account for all possible causes of neuropathy like alcohol abuse, hypothyroidism, renal failure, and vitamin B12 supplementation, in the exclusion criteria. In the study referred to by the reader, there was a change only in the Holo-Tc II levels and no significant alteration in vitamin B12 levels on calcium supplementation.[6] Although we have also referenced this small study (n = 14 metformin, n = 7 control) in the discussion, we did not find systematic evidence of a larger cohort for prevention and reversal of metformin-induced B12 deficiency by calcium supplementation, hence we did not actively exclude people taking calcium supplements from our study. C) The unit of the cumulative metformin dose provided in the figure is in grams.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".