The Dose-Response Effect of Calcium Supplementation on Biomarkers Associated With the Pathogenesis of Preeclampsia in Tanzania
Bibliographic record
Abstract
Results: After screening 6,425 abstracts, 56 studies in 66 reports were included, reporting >200 analyses.Sixty-three countries were represented, including 39 low-and middle-income economies (LMICs).Most frequent interventions were: vitamin A, folic acid, iron, and iodine added to cereal grains/ products (e.g., flours) and condiments (e.g., oils, sugar, salt).Models were heterogeneous and employed various perspectives.Most evaluations (58%; 135/232) had ICERs less than $150 per disability-adjusted life year (DALY) averted (or healthy life year gained).We found 87% (201/232) overall were within a hypothetical CE threshold of "50% GDP pc".With an example "35% GDP pc" level among LMICs, 84% (190/227) were estimated to be cost-effective; and 71% (37/52) were less than "20% GDP pc" among low-income countries.Additionally, six out of eight costutility studies' ICERs were dominant.Moreover, 47 total unique benefit-cost ratios found benefits outweighed costs, ranging from 1⋅50:1 to 100⋅6:1.Conclusions: Food fortification programs are likely costeffective in the majority of contexts.While cost-effectiveness evaluations are specific to local factors and methodology, this research can assist with evidence-informed decision-making for global health policy and priority setting, particularly in resourceconstrained economies.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".