Reported Outcomes in Perinatal Iron Deficiency Anemia Trials: A Systematic Review
Bibliographic record
Abstract
BACKGROUND/AIMS: Iron deficiency (ID) and iron deficiency anemia (IDA) are global health concerns associated with adverse perinatal effects. Despite efforts taken at the international level, there is no consensus on unified prevention/treatment strategies, largely stemming from inconsistencies of outcome reporting. Our objective was to comprehensively assess outcome reporting perinatal iron intervention trials as Phase 1 of core outcome set (COS) development to inform future research. METHODS: Systematic search in MEDLINE, EMBASE, Cochrane Databases, and CINAHL (January 2000 - April 2016), with inclusion of trials involving pregnant or postpartum women with ID/IDA confirmed before recruitment. Articles were independently screened and selected by 2 reviewers; data were extracted in duplicate. Quality was assessed using published scoring systems. Outcome definitions and measurement methods were tabulated. RESULTS: Of 7,046 citations, 33 randomized controlled trials were included. Sixty-nine reported outcomes were categorized into 8 domains. High methodological quality characterized 25 (76%) studies. Reporting quality was low in 16 (49%), moderate in 13 (39%), and high in 4 (12%) studies. Variation was greatest for outcome definition, timing of assessment and measurement methods. CONCLUSION: This review identifies a comprehensive long-list of outcomes reported of perinatal iron interventions for ID/IDA. Beyond highlighting existing variation in outcome reporting, it provides a foundation for development of a COS for future trials.
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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 | 0.039 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".