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Record W2992031486 · doi:10.1097/moo.0000000000000595

The relationship of chronic rhinosinusitis and depression

2019· review· en· W2992031486 on OpenAlexaff
Kristine A. Smith, Jeremiah A. Alt

Bibliographic record

VenueCurrent Opinion in Otolaryngology & Head & Neck Surgery · 2019
Typereview
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDepression (economics)MedicineComorbidityChronic rhinosinusitisQuality of life (healthcare)PsychiatryInternal medicinePhysical therapyIntensive care medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The aim of this article is to identify and describe the relationship between chronic rhinosinusitis (CRS) and comorbid depression, including the patient factors that may increase the risk of depression. The impact of comorbid depression on quality of life, response to treatment and healthcare utilization will also be assessed. RECENT FINDINGS: CRS is associated with a significantly increased prevalence of depression, where 9-26% of patients with CRS will have physician-diagnosed depression. An additional 40% will have undiagnosed depression that can be identified through screening tools. Patients without polyps are more likely to experience comorbid depression, as are patients with significant sleep dysfunction, olfactory dysfunction, and pain. CRS symptoms do improve with medical and surgical therapy in depressed patients, though baseline and posttreatment scores are worse. A similar degree of benefit from therapy is seen in both depressed and nondepressed patients. CRS treatment does seem to improve depression, whereas the effect of depression specific therapy is unknown. Depressed patients have a significantly larger economic burden because of their increased healthcare utilization and productivity losses. SUMMARY: Depression is a highly prevalent and impactful comorbidity in patients with CRS. Increased awareness of this relationship may improve patients' overall quality of care.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.610
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.139
GPT teacher head0.404
Teacher spread0.264 · 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 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

Citations26
Published2019
Admission routes1
Has abstractyes

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