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Record W4396620324 · doi:10.1177/03000605241248039

Association between the Dietary Inflammatory Index and fracture risk in older adults: a systematic review and meta-analysis

2024· review· en· W4396620324 on OpenAlexaboutno aff
Xiaojuan Zheng, Wenhui Li, Yonglong Yan, Zhaojie Su, Xuelin Huang

Bibliographic record

VenueJournal of International Medical Research · 2024
Typereview
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsnot available
FundersNatural Science Foundation of Xiamen City
KeywordsMedicineMeta-analysisInternal medicineObservational studyRelative riskCohort studyConfidence intervalStudy heterogeneityRandom effects modelProspective cohort studyEpidemiology

Abstract

fetched live from OpenAlex

OBJECTIVE: We explored correlations between the Dietary Inflammatory Index (DII) and fracture risk in older adults. METHODS: We systematically searched MEDLINE, PubMed, Science Direct, Scopus, and CNKI for all relevant epidemiological studies published through October 16, 2023. Because observational studies were included in the meta-analysis, we used a random-effects model to pool the study-specific effect sizes and 95% confidence intervals (CIs). We assessed study quality using the Newcastle-Ottawa scale. This meta-analysis was registered in PROSPERO. RESULTS: = 87.1%, indicating significant between-study heterogeneity; hence, the random-effects model was adopted to generate the combined effect size. We found that the DII was positively associated with fracture (relative risk: 1.188, 95% CI: 1.043-1.354). This result was further confirmed in leave-one-out sensitivity analysis. CONCLUSIONS: Our study provides evidence suggesting that diets high in pro-inflammatory components might increase the fracture risk among older people. Decreased consumption of pro-inflammatory foods and increased consumption of anti-inflammatory foods are suggested to prevent adverse fracture outcomes. More prospective studies involving both sexes are warranted to verify the results.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.031
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.541
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.007
Insufficient payload (model declined to judge)0.0010.000

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 head0.096
GPT teacher head0.488
Teacher spread0.392 · 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 teacher head, not a consensus.

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

Citations6
Published2024
Admission routes1
Has abstractyes

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