Impact of BMI on serum uric acid levels in females in local population.
Bibliographic record
Abstract
Body mass index is the fat content of body whereas hyperuricemia is the condition when serum uric acid level crosses the optimum normal level. Increase in BMI may influence serum uric acid. Objective: To find out the relationship of BMI with serum uric acid level in local population. Study Design: Cross sectional study. Setting: Lady Aitchison hospital Lahore. Period: March 2017- August 2017. Material and Methods: Written informed consent was taken prior to data collection. Detailed history was asked and weight in kilograms and height in meters was noted. Body mass index was calculated. Uric acid was measured by uricase method after taking 1 ml venous blood under aseptic measures. Results: Body mass index and serum uric acid of the subjects were compared with the standard values that were 24.99kg/m2 and 5.7mg/dl and statistical analysis showed a significant difference (p-0.023 and p-0.000) respectively. Simple linear regression revealed 0.391 units change in serum uric acid level with one unit change in BMI. Conclusion: Serum uric acid increased with increase in BMI.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".