Lack of uniformity in screening, diagnosis and management of gestational diabetes mellitus among health practitioners across major cities of Pakistan
Bibliographic record
Abstract
OBJECTIVE: To determine knowledge, attitude and practice (KAP) regarding management of Gestational Diabetes Mellitus (GDM) among Health Care Providers in major cities of Pakistan. METHODS: A knowledge, attitude and practice (KAP) questionnaire based study was conducted in major cities in Pakistan from health care providers in public and private hospitals and clinics. Questionnaires were provided to the health care providers regarding screening, diagnosis and management of patients with GDM. Data analysis was done using IBM SPSS 20. RESULTS: A total of 210 doctors took part in the study. 55 (26%) reported using fasting blood glucose as screening test for GDM whereas 129(61.4%) respondents used Oral Glucose Tolerance based WHO criteria for diagnosing GDM. Thirty six (17%) and 98(46.7%) doctors referred their patients to Gynecologists. For treating GDM, 64(30.5%) doctors prescribed insulin (NPH/Regular, 70/30 Mix). 112 (53.5) doctors used combination of capillary glucose by glucometer and plasma blood glucose tests for monitoring of glycemic control of patients with GDM. CONCLUSION: There is lack of agreed screening tests and criteria for diagnosis and management of GDM patients. Doctors need to be educated to follow evidence based diagnostic and management guidelines so that GDM patients can be effectively managed. Recently released South Asian Federation Societies and Pakistan Endocrine Society guidelines could be much needed consensus guidelines for doctors to apply in their daily practice to improve GDM diagnosis and treatment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".