Using computer expert system to solve complications primarily due to low and excessive birth weights at delivery: Strategies to reviving the ageing and diminishing population
Bibliographic record
Abstract
The world now lives in an ageing society. Significant empirical evidence from the literature revealed that the world’s total fertility rate (TFR) estimate has experienced a declining trend from 5.30 in 1963 to 2.3 in 2020. The TFR for 2023 for the United States was 1.84, Canada 1.57, United Kingdom 1.63, Germany 1.58, Japan 1.39, China 1.45, Nigeria 4.57, India 2.07, Ghana 3.61, with Taiwan 1.09 and Niger 6.73 having the least and highest respectively. Non-adherence to God's childbearing principles, a female's age when she has her first child, educational opportunities, access to family planning, and government acts and policies affecting childbearing are all factors that may influence TFR. Despite the danger posed by the declining TFR trend, childbearing challenges and complications primarily due to low and excessive birth weights at delivery have heightened this danger. This study highlights strategies to leverage the provision of a computerized expert system (ES) solution to the problems of complications primarily due to low and excessive birth weights at delivery. A Knowledge-Based System (KBS) framework to simulate the problem-solving behavior of an expert in a narrow domain or discipline to unite the accumulated expertise of individual disciplines such as gynecology, ultrasonography, computer software design, and engineering was adopted. Fetal weight has been found to be a function of fetal head circumference (HC), femur length (FL), abdominal circumference (AC), and biparietal diameter (BPD) and predictable with a polynomial equation. Also, fetal age has been found to be a function of fetal weight and predictable by an equation. The equations for the determination of conception date and delivery dates were derived. The Expert System (ES) primarily estimates fetal parameters (fetal weight, fetal age or gestational age, conception date, and delivery date) in the first trimester using ultrasonographic fetal biometric data. The result of this study may eliminate or reduce the occurrence of potential complications associated with the birth of both small and excessively large fetuses, thereby contributing to reviving the world declining population.
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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 | 0.004 | 0.012 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".