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Record W2999488735 · doi:10.1177/0194599819900263

Inferior Meatus Augmentation Procedure (IMAP) to Treat Empty Nose Syndrome: A Pilot Study

2020· article· en· W2999488735 on OpenAlexaff
Andrew Thamboo, Sachi S. Dholakia, Nicole A. Borchard, Vishal S. Patel, Navarat Tangbumrungtham, Nathalia Velasquez, Zhenxiao Huang, David Zarabanda, Tsuguhisa Nakayama, Jayakar V. Nayak

Bibliographic record

VenueOtolaryngology · 2020
Typearticle
Languageen
FieldMedicine
TopicNasal Surgery and Airway Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineMeatusNoseQuality of life (healthcare)AnxietyStatistical significancePhysical therapySurgeryInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Our understanding of empty nose syndrome (ENS) continues to evolve. Prior studies evaluating airway augmentation to treat ENS did not use validated disease‐specific questionnaires, making the true impact of these surgeries unclear. We present a case series of 10 patients with ENS (11 procedures) who underwent the inferior meatus augmentation procedure (IMAP) between September 2014 and May 2017. Subjective outcomes of IMAP included comparisons of preoperative and postoperative assessments (1 week, 1 month, 3 months, 6 months) using the Empty Nose Syndrome 6‐item Questionnaire (ENS6Q), Patient Health Questionnaire–9 (PHQ‐9), Generalized Anxiety Disorder 7‐item Scale (GAD‐7), and Sino‐Nasal Outcome Test–22 (SNOT‐22). The decrement in ENS6Q scores observed maintained statistical significance at 6 months (P ≤. 001). Similar results were achieved with PHQ‐9, GAD‐7, and SNOT‐22 (P ≤. 01, P ≤. 01, P ≤. 001, respectively). IMAP can dramatically improve the quality of life of ENS patients regarding both ENS‐specific symptoms and psychological well‐being.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.299
Teacher spread0.254 · 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 designNon-randomized trial
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

Citations23
Published2020
Admission routes1
Has abstractyes

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