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Record W4400534855 · doi:10.1001/jamaoto.2024.1854

Olfactory Impairment and Frailty

2024· review· en· W4400534855 on OpenAlexaboutno aff
Brian Sheng Yep Yeo, Jun He Chan, Benjamin Kye Jyn Tan, Xuandao Liu, Laura Tay, Neville Wei Yang Teo, Tze Choong Charn

Bibliographic record

VenueJAMA Otolaryngology–Head & Neck Surgery · 2024
Typereview
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOdds ratioObservational studyCochrane LibraryOddsCINAHLCross-sectional studyMeta-analysisGerontologyInternal medicineLogistic regressionPsychological interventionPsychiatryPathology

Abstract

fetched live from OpenAlex

Importance: Olfactory impairment (OI) and frailty are prevalent conditions associated with aging, but studies investigating their association with each other have been discordant. Objective: To summarize current evidence surrounding the association between OI and frailty. Data Sources: PubMed, Embase, Cochrane Library, SCOPUS, and CINAHL from inception to November 28, 2023. Study Selection: This study included observational studies investigating the association between objectively or subjectively assessed OI and objectively evaluated frailty among adults. Data Extraction and Synthesis: Two independent authors extracted data into a structured template. Maximally adjusted estimates were pooled using a random-effects model, and statistical heterogeneity was evaluated using I2 values. Additional prespecified subgroup and sensitivity analyses were performed. This study used the Newcastle-Ottawa Scale for bias assessment and the Grading of Recommendations Assessment, Development and Evaluation framework for overall evidence quality evaluation. Main Outcomes and Measures: The primary outcome was the cross-sectional association between OI and frailty, for which the odds of frailty were compared between participants with and without OI. The secondary outcome was the cross-sectional association between frailty and OI, for which the odds of OI were compared between participants with and without frailty. Results: This study included 10 studies with 10 624 patients (52.9% female; mean [SD] age, 62.9 [9.6] years). The Newcastle-Ottawa Scale score of studies ranged from low to moderate. Grading of Recommendations Assessment, Development and Evaluation scores ranged from low to moderate. OI was associated with a 2.32-fold (odds ratio [OR], 2.32; 95% CI, 1.63-3.31; I2 = 0%) greater odds of frailty compared with individuals with healthy olfactory function. The odds of OI was progressively greater with categorical frailty status, with a 1.55-fold (OR, 1.55; 95% CI, 1.32-1.82; I2 = 0%), 2.28-fold (OR, 2.28; 95% CI, 1.96-2.65; I2 = 0%), and 4.67-fold (OR, 4.67; 95% CI, 2.77-7.86; I2 = 0%) increase in odds for individuals with prefrailty, frailty, and the most frailty, respectively, compared with robust individuals. The results demonstrated stability in subgroup analyses (geographical continent of study, objective vs subjective olfactory assessment) and sensitivity tests. Conclusions and Relevance: The results of this systematic review and meta-analysis suggest that there is an association between OI and frailty, with an increase in the odds of OI with worsening categorical frailty status among individuals with prefrailty, frailty, and the most frailty. OI may be a potential biomarker for frailty. Future studies could delve into whether OI may be a modifiable risk factor for frailty.

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)0.006
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.050
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0050.004
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.221
GPT teacher head0.336
Teacher spread0.115 · 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

Citations23
Published2024
Admission routes1
Has abstractyes

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