Gestational Malnutrition is Still the Skeleton in the Closet: Where Are the Malnutrition Risk Screening Criteria?
Bibliographic record
Abstract
In 2020 there were 3,613,647 live births in the US. Fifty to 90% of those pregnancies were affected by nausea and vomiting of pregnancy (NVP) while the most severe NVP, known as hyperemesis gravidarum (HG) affected 1.5- 3.0% of those gravidas. In 2014 58,436 gravidas were hospitalized with HG. While hospital admissions for HG were down 42%, the number seen in Emergency departments rose by 27.7%. Although HG has been viewed as a positive predictor of a favorable pregnancy outcome, patients who also demonstrate weight loss and electrolyte disturbance may be a distinct entity and at greater risk for growth retardation and fetal anomalies. Poorly managed HG can result in compounded maternal injuries, increased rates of therapeutic abortions and suicide ideation, as well as a high rate of fetal loss. This study focuses on one of the most severe catastrophes originating from HG: Wernicke’s encephalopathy (WE) continues to be an under-recognized and often misunderstood disease in all populations. The acknowledged cause of WE is vitamin B1 or thiamin deficiency, a specific form of malnutrition. Unfortunately, this syndrome is most often recognized at autopsy, especially among non-alcoholics. One manifestation of WE is cognitive dysfunction which may explain the increase in suicidal ideation and/or elected terminations. The current mounting legislation of pregnancy termination limits will complicate the decision to abort for critically ill gravidas and their medical providers. However, antecedent events to WE are extremely poor nutritional intake leading to excessive weight loss and delayed nutritional interventions which can mitigate these situations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".