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Record W1991618423 · doi:10.2310/7070.2005.03122

Partial Inferior Turbinectomy Using the Microdébrider

2005· article· en· W1991618423 on OpenAlexvenueno aff
David Wexler, Itzhak Braverman

Bibliographic record

VenueThe Journal of Otolaryngology · 2005
Typearticle
Languageen
FieldMedicine
TopicNasal Surgery and Airway Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTurbinectomyMedicineSurgeryNoseEndoscopyIrritationTurbinates

Abstract

fetched live from OpenAlex

BACKGROUND: A microdébrider was selected to accomplish partial inferior turbinectomy, allowing for controlled and rapid removal of hypertrophic soft tissue while preserving the general turbinate form. OBJECTIVE: To assess the clinical outcome, healing, and any adverse consequences from the microdébrider partial turbinectomy procedure. SETTING: A public hospital in north-central Israel. DESIGN: A nonrandomized prospective study of 35 adults who were referred for nasal airway surgery, including turbinectomy. METHODS: All patients underwent bilateral inferior turbinate reduction with the microdébrider, with removal of mucosa from the medial and inferior portions of the inferior turbinates. Detailed follow-up was accomplished at 4 or more months postoperatively, including a visual analogue scale questionnaire and videoendoscopy. For seven patients, pre- and postoperative mucosal biopsies were available to evaluate healing and epithelial regeneration. RESULTS: Nasal endoscopy showed well-healed turbinate membranes and preservation of the turbinate form, with widening of the inferomedial nasal airway space. Subjective nasal patency improved after surgery, p < .01, and the subjective sense of smell was improved, p < .01, without associated crusting, pain, irritation, sneezing, or dryness. Postoperative biopsies showed subepithelial fibrosis and regenerated epithelium, generally of respiratory differentiation. CONCLUSION: Inferior turbinate reduction can be accomplished efficiently with the microdébrider device, without undue side effects. SIGNIFICANCE: Further experience and long-term follow-up with this technique are warranted.

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)0.000
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 score0.410
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.028
GPT teacher head0.297
Teacher spread0.268 · 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

Citations25
Published2005
Admission routes1
Has abstractyes

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