Simple method for identification of women at risk of gestational diabetes mellitus in Arusha urban, Tanzania
Bibliographic record
Abstract
BACKGROUND: Screening for gestational diabetes mellitus in Tanzania is challenged by limited resources. Therefore, this study aimed to develop a simple method for identification of women at risk of gestational diabetes mellitus in Arusha urban, Tanzania. METHODS: This study used data from a cross sectional study, that was conducted between March and December 2018 in Arusha District involving 468 pregnant women who were not known to have diabetes before pregnancy. Urine glucose was tested using urine multistics and blood glucose levels by Gluco-Plus™ and diagnosed in accordance with the World Health Organization's criteria. Anthropometrics were measured using standard procedures and maternal characteristics were collected through face-to-face interviews using a questionnaire with structured questions. Univariate analysis assessed individual variables association with gestational diabetes mellitus where variables with p-value of < 0.05 were included in multivariable analysis and predictors with p-value < 0.1 remained in the final model. Each variable was scored based on its estimated coefficients and risk scores were calculated by multiplying the corresponding coefficients by ten to get integers. The model's performance was assessed using c-statistic. Data were analyzed using Statistical Package for Social Science™. RESULTS: The risk score included body fat ≥ 38%, delivery to macrosomic babies, mid-upper arm circumference ≥ 28 cm, and family history of type 2 diabetes mellitus. The score correctly identified 98% of women with gestational diabetes with an area under the receiver operating characteristic curve of 0.97 (95% CI 0.96-0.99, p < 0.001), sensitivity of 0.98, and specificity of 0.46. CONCLUSION: The developed screening tool is highly sensitive and correctly differentiates women with and without gestational diabetes mellitus in a Tanzanian sub-population.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".