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Record W3087923353 · doi:10.4103/ijpvm.ijpvm_243_19

Beneficial Role of Calcium in Premenstrual Syndrome: A Systematic Review of Current Literature

2020· review· en· W3087923353 on OpenAlexaboutno aff
Arman Arab, Nahid Rafie, Gholamreza Askari, Mina Taghiabadi

Bibliographic record

VenueInternational Journal of Preventive Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicMenstrual Health and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsJadad scaleMedicineObservational studySystematic reviewScopusMeta-analysisMEDLINEInternal medicineCochrane Library

Abstract

fetched live from OpenAlex

Since premenstrual syndrome (PMS) is one of the most common and debilitating disorders in women, risk factor modification is an urgent health priority. Therefore, this systematic review aimed to summarize and discuss the outcomes of observational and interventional studies in humans regarding the relationship between Calcium and PMS. PubMed, Scopus, ISI web of sciences and Google scholar were searched up to January 2019 to identify relevant studies. The Newcastle-Ottawa and Jadad scales were used for quality assessment. A total of 14 studies (8 interventional and 6 observational) met our inclusion criteria. Majority of the studies showed that not only serum calcium levels are lower in PMS subjects, but also calcium supplementation could significantly improve the incidence of PMS and its related symptoms. This systematic review suggests a beneficial role for calcium in PMS subjects. However, in order to draw a firm link between calcium and PMS, further dose-response clinical trials with larger sample size and better methodological design are warranted.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0060.006
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.050
GPT teacher head0.437
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations27
Published2020
Admission routes1
Has abstractyes

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