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Record W2048623202 · doi:10.3390/pharmaceutics6020249

Drug-Eluting Nasal Implants: Formulation, Characterization, Clinical Applications and Challenges

2014· article· en· W2048623202 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePharmaceutics · 2014
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvanced Drug Delivery Systems
Canadian institutionsQueen Elizabeth II Health Sciences CentreCapital District Health Authority
Fundersnot available
KeywordsMedicineSinusitisChronic rhinosinusitisIntensive care medicineImplantChronic sinusitisDrugSurgeryPharmacology

Abstract

fetched live from OpenAlex

Chronic inflammation and infection of the nasal sinuses, also referred to as Chronic Rhinosinusitis (CRS), severely affects patients' quality of life. Adhesions, ostial stenosis, infection and inflammation relapses complicate chronic sinusitis treatment strategies. Drug-eluting stents, packings or implants have been suggested as reasonable alternatives for addressing these concerns. This article reviewed potential drug candidates for nasal implants, formulation methods/optimization and characterization methods. Clinical applications and important considerations were also addressed. Clinically-approved implants (Propel™ implant, the Relieva stratus™ MicroFlow spacer, and the Sinu-Foam™ spacer) for CRS treatment was an important focus. The advantages and limitations, as well as future considerations, challenges and the need for additional research in the field of nasal drug implant development, were discussed.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.215
GPT teacher head0.475
Teacher spread0.260 · 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