Routine Iron Supplementation and Screening for Iron Deficiency Anemia in Pregnancy
Bibliographic record
Abstract
Letters1 September 2015Routine Iron Supplementation and Screening for Iron Deficiency Anemia in PregnancyAmy G. Cantor, MD, MPH, Christina Bougatsos, MPH, and Marian McDonagh, PharmDAmy G. Cantor, MD, MPHFrom Pacific Northwest Evidence Based Practice Center, Oregon Health & Science University, Portland, Oregon., Christina Bougatsos, MPHFrom Pacific Northwest Evidence Based Practice Center, Oregon Health & Science University, Portland, Oregon., and Marian McDonagh, PharmDFrom Pacific Northwest Evidence Based Practice Center, Oregon Health & Science University, Portland, Oregon.Author, Article, and Disclosure Informationhttps://doi.org/10.7326/L15-5132-2 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:Dr. Kane suggests that we need to further understand adherence to accurately interpret trial results for iron supplementation in pregnant women. We provided data on adherence (or rather nonadherence) in Appendix Table 4. For the supplementation studies, adherence—usually based on pill counts or an equation involving pill counts—was variably reported and ranged from 54% to 98%. Seven of 10 studies reported adherence. Although 6 studies found no difference in adherence between the groups, the seventh showed significantly greater adherence in the iron supplementation group (97.8% vs. 83.9%; ...Reference1. Meier PR, Nickerson HJ, Olson KA, Berg RL, Meyer JA. Prevention of iron deficiency anemia in adolescent and adult pregnancies. Clin Med Res. 2003;1:29-36. [PMID: 15931282] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAuthors: Amy G. Cantor, MD, MPH; Christina Bougatsos, MPH; Marian McDonagh, PharmDAffiliations: From Pacific Northwest Evidence Based Practice Center, Oregon Health & Science University, Portland, Oregon.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M14-2932. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoRoutine Iron Supplementation and Screening for Iron Deficiency Anemia in Pregnancy: A Systematic Review for the U.S. Preventive Services Task Force Amy G. Cantor , Christina Bougatsos , Tracy Dana , Ian Blazina , and Marian McDonagh Routine Iron Supplementation and Screening for Iron Deficiency Anemia in Pregnancy Robert C. Kane Routine Iron Supplementation and Screening for Iron Deficiency Anemia in Pregnancy Robert C. Kane Metrics Cited byNutritional Gaps and Supplementation in the First 1000 DaysIron Requirements and Adverse Pregnancy OutcomesGaps in evidence regarding iron deficiency anemia in pregnant women and young children: summary of US Preventive Services Task Force recommendationsIron status of North American pregnant women: an update on longitudinal data and gaps in knowledge from the United States and Canada 1 September 2015Volume 163, Issue 5Page: 400KeywordsDisclosureIntent to treat analysisIron deficiency anemiaObservational studiesPregnancy ePublished: 1 September 2015 Issue Published: 1 September 2015 Copyright & PermissionsCopyright © 2015 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".