Impact of intrauterine growth restriction on long-term health
Bibliographic record
Abstract
PURPOSE OF REVIEW: Intrauterine growth restriction (IUGR) is responsible for the higher rates of fetal, perinatal, and neonatal morbidity and mortality. This review details the IUGR risk factors, its short and long-term sequel, the mechanism underlying the long-term consequences, and the strategies to tackle IUGR burden. RECENT FINDINGS: Short-term consequences of IUGR involve metabolic, thermal, and hematological disturbances leading to morbidities. Long term consequences due to changes in the fetal nutritional environment is associated with increased risk of developing metabolic syndrome and cardiovascular disease, systolic hypertension, obesity, insulin resistance, and diabetes type II in adulthood. There are no effective therapies to reverse IUGR, and antenatal management is aimed at determining the ideal time and mode of delivery. In order to prevent complications associated with IUGR, it is important to first detect the condition and institute appropriate surveillance to assess fetal well-being coupled with suitable intervention in case of fetal distress. SUMMARY: Reliable prediction of IUGR may be achieved by combining clinical risk factors with Doppler abnormalities, fetal growth, and biomarkers. If this can be achieved, there is potential to reduce future perinatal morbidity, mortality and long-term consequences, but steps geared toward the prevention of IUGR are of unparalleled importance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".