Identification and treatment of iron‐deficiency anemia in pregnancy and postpartum: A systematic review and quality appraisal of guidelines using AGREE II
Bibliographic record
Abstract
BACKGROUND: Several international guidelines provide recommendations for the optimal management of iron-deficiency anemia (IDA) in the pregnant and postpartum populations. OBJECTIVES: To review the quality of guidelines containing recommendations for the identification and treatment of IDA in pregnancy and postpartum using the Appraisal of Guidelines for Research and Evaluation II (AGREE II) instrument and to summarize their recommendations. SEARCH STRATEGY: PubMed, Medline, and Embase databases were searched from inception to August 2, 2021. A web engine search was also performed. SELECTION CRITERIA: Clinical practice guidelines that focused on the management of IDA in pregnancy and/or postpartum populations were included. DATA COLLECTION AND ANALYSIS: Included guidelines were appraised using AGREE II independently by two reviewers. Domain scores greater than 70% were considered high-quality. Overall scores of six or seven (out of a possible seven) were considered high-quality guidelines. Recommendations on IDA management were extracted and summarized. MAIN RESULTS: Of 2887 citations, 16 guidelines were included. Only six (37.5%) guidelines were deemed high-quality and were recommended by the reviewers. All 16 (100%) guidelines discussed the management of IDA in pregnancy, and 10 (62.5%) also included information on the management of IDA in the postpartum period. CONCLUSIONS: The complex interplay of racial, ethnic, and socioeconomic disparities was rarely addressed, which limits the generalizability of the recommendations. In addition, many guidelines failed to identify barriers to implementation, strategies to improve uptake or iron treatment, and resource and cost implications of clinical recommendations. These findings highlight important areas to target future work.
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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 | 0.089 | 0.308 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.011 |
| Bibliometrics | 0.033 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".