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Record W3015347320 · doi:10.5152/iao.2020.7688

Long-Term Outcomes from Blind Sac Closure of the External Auditory Canal: Our Institutional Experience in Different Pathologies

2020· article· en· W3015347320 on OpenAlexaff
Mordechai Kraus, Fatemeh Hassannia, Michael J. Bergin, Khalid Al Zaabi, John Rutka

Bibliographic record

VenueThe Journal of International Advanced Otology · 2020
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineSurgeryCholesteatomaComplicationRetrospective cohort studyOsteoradionecrosisLeakCerebrospinal fluid leakSkullMeatusCerebrospinal fluidRadiation therapyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: This study reports long-term results of blind sac closure of the external auditory canal performed for various pathologies, compares the complication rates and the need for revision surgery. MATERIALS AND METHODS: This study is a retrospective review. Ninety-six cases of blind sac closure performed for various pathologies were included in this study. The primary pathologies included extensive mucosal disease in an open mastoid cavity, cholesteatoma, skull base lesion, cerebrospinal fluid leak, and osteoradionecrosis of the temporal bone. Preoperative history, postoperative complications, and the need for revision surgery were evaluated. RESULTS: The most common indication for blind sac closure in our series involved skull base lesions (62.5%). The mean follow-up period was 46 months (4 months - 20 years). The total complication rate related to blind sac closure was 10.4%. The median time between surgery and long-term complications was 5.5 years. Patients with chronic mucosal disease had the highest rate of complications. CONCLUSION: Blind sac closure of external meatus can be effectively performed for different pathologies. Long-term follow-up with patients is necessary. Patients with chronic mucosal disease have the highest complication rates.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.256

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 head0.050
GPT teacher head0.334
Teacher spread0.283 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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