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Record W2132719084 · doi:10.1186/1710-1492-5-9

Treating rhinitis in the older population: special considerations

2009· article· en· W2132719084 on OpenAlexvenueno aff
Raymond G. Slavin

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2009
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePolypharmacyNoseNasal decongestantIntensive care medicineDermatologySurgeryAnesthesia

Abstract

fetched live from OpenAlex

Rhinitis in the elderly is a common but often neglected condition. Structural changes in the nose associated with aging, predisposes the elderly to rhinitis. There are a number of specific factors that affect medical treatment of the elderly including polypharmacy, cognitive dysfunction, changes in body composition, impairment of liver and renal function and the cost of medications in the face of limited resources. Rhinitis in the elderly can be placed in several categories and treatment should be appropriate for each condition. The most important aim is to moisten the nasal mucosa since the nose of the elderly is so dry. Great caution should be used in treatment with first generation antihistamines and decongestants. Medications generally well tolerated by the elderly are second generation antihistamines, intra-nasal anti-inflammatory agents, leukotriene modifiers and iprapropium nasal spray.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.117
GPT teacher head0.335
Teacher spread0.217 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations38
Published2009
Admission routes1
Has abstractyes

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