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Record W4224326642 · doi:10.1001/jamaoto.2022.0263

Association of Olfactory Impairment With All-Cause Mortality

2022· review· en· W4224326642 on OpenAlexaboutno aff
Natalie Yan‐Lin Pang, Harris Jun Jie Muhammad Danial Song, Benjamin Kye Jyn Tan, Jun Tan, Ashley Si Ru Chen, Anna See, Shuhui Xu, Tze Choong Charn, Neville Wei Yang Teo

Bibliographic record

VenueJAMA Otolaryngology–Head & Neck Surgery · 2022
Typereview
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineObservational studyMeta-analysisCohort studyPublication biasCochrane LibrarySystematic reviewHazard ratioEpidemiologyCohortMEDLINEInternal medicineConfidence intervalBiology

Abstract

fetched live from OpenAlex

Importance: Olfactory impairment is highly prevalent and associated with multiple comorbidities, including neurodegenerative, cardiovascular, nutritional, and immune disorders. However, epidemiologic associations between olfactory impairment and mortality are discordant. Objective: To systematically clarify the epidemiologic associations between olfactory impairment and mortality. Data Sources: The PubMed, Embase, and Cochrane Library databases were searched from inception to August 13, 2021. Study Selection: Two blinded reviewers selected observational studies published as full-length, English-language articles in peer-reviewed journals that reported the presence or severity of chronic olfactory impairment, whether objectively measured or self-reported, in association with any mortality estimate, among adults aged 18 years or older. Data Extraction and Synthesis: Two reviewers independently extracted data, evaluated study bias using the Newcastle-Ottawa Scale, and appraised the quality of the evidence using the Grading of Recommendations Assessment, Development and Evaluation framework, following Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines and a PROSPERO-registered protocol. Maximally adjusted estimates were pooled using mixed-effects models, heterogeneity was measured using I2 statistics, sources of heterogeneity were investigated using meta-regression and subgroup meta-analyses, and publication bias was qualitatively and quantitatively assessed. Main Outcomes and Measures: Hazard ratios for all-cause mortality. Results: One retrospective cohort study and 10 prospective cohort studies (with a total of 21 601 participants) from 1088 nonduplicated records were included. Ten studies had a low risk of bias, whereas 1 study had a moderate risk; exclusion of the latter did not alter conclusions. Nine studies were included in the meta-analysis. Olfactory loss was associated with a significantly higher pooled hazard of all-cause mortality (hazard ratio, 1.52; 95% CI, 1.28-1.80; I2 = 82%). Meta-regression sufficiently explained heterogeneity, with longer mean follow-up duration weakening the pooled association, accounting for 91.3% of heterogeneity. Self-reported and objective effect sizes were similar. Associations were robust to trim-and-fill adjustment and the Egger test for publication bias. The overall quality of evidence was moderate. Conclusions and Relevance: The findings of this systematic review and meta-analysis suggest that olfactory impairment is associated with all-cause mortality and may be a marker of general health and biological aging. Further research is required to establish the underlying mechanisms and the scope for interventions.

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)0.014
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.050
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0060.006
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.269
GPT teacher head0.337
Teacher spread0.068 · 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 designObservational
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

Citations43
Published2022
Admission routes1
Has abstractyes

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