Comparison of neurocognitive changes among newly diagnosed tuberculosis patients with and without dysglycaemia
Bibliographic record
Abstract
Abstract Background Diabetes often occurs together with tuberculosis (TB) and both may affect each other negatively. Diabetes may be associated with neurocognitive dysfunctioning in affected patients and may negatively impact treatment adherence and outcomes. This study compared the neurocognitive status between newly diagnosed smear positive tuberculosis patients with dysglycaemia and those with normoglycaemia. Methods The current study was a cross-sectional study involving one hundred and forty-six (146) newly diagnosed smear positive TB patients. Oral glucose tolerance test (OGTT) was performed and the results were categorized as either normoglycaemia, impaired glucose tolerance (IGT), impaired fasting glucose (IFG) or diabetes. Neurocognitive functioning among study participants was assessed at the time of TB diagnosis using Cognitive Failure Questionnaire (CFQ), Montreal Cognitive Assessment tool (MoCA), California Verbal Learning Test (CVLT), Brief Symptom Inventory (BSI) and the Spitzer Quality of Life Index (QLI). Results The mean age of the participants ( n = 146) was 38.7 years with 78.8% being males and 21.2% females. Using the fasting blood glucose test, the prevalence of impaired fasting glucose and diabetes were 5.5 and 3.4% respectively, both representing a total of 13 out of the 146 participants; whilst the prevalence of impaired glucose tolerance and diabetes using 2-h post-glucose values were 28.8 and 11.6% respectively, both representing a total of 59 out of the 146 participants. There were no significant differences in the mean scores on the neurocognitive measures between the dysglaycaemia and normoglycamic groups using fasting plasma glucose (FPG). However, there were significant differences in the mean scores between the dysglycaemia and normal groups using 2-h postprandial (2HPP) glucose values on Phobic Anxiety (Normal, Mean = 0.38 ± 0.603; dysglycaemia, Mean = 0.23 ± 0.356; p = 0.045), and Montreal Cognitive Assessment (MoCA) scores (17.26 ± 5.981 vs. 15.04 ± 5.834, p = 0.037). Conclusion Newly diagnosed smear positive patients with dysglycaemia were associated with significantly lower mean cognitive scores and scores on phobic anxiety than those with normoglyacaemia. The latter finding must be further explored.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".