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Record W2433412670

Adjustable laryngeal implant for unilateral vocal cord paralysis.

2008· article· en· W2433412670 on OpenAlexaff
Apostolos Christopoulos, Issam Saliba, Louis Péloquin, Christian Ahmarani

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsVocal fold paralysisMedicineGynecologyImplantHumanitiesSurgeryPhilosophyParalysis
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Laryngeal framework surgery has been widely accepted as a definitive treatment for unilateral vocal cord paralysis (UVCP). An adjustable laryngeal implant has been developed for precise medialization of the paralyzed vocal cord. OBJECTIVES: To describe the implant and surgical technique and to evaluate the effect of vocal cord medialization using an adjustable laryngeal implant on the quality of life of patients with UVCP. PATIENTS AND METHODS: Fifty-three patients with UVCP who had undergone medialization with the adjustable laryngeal implant were identified. All patients completed the Voice Handicap Index (VHI) quality of life questionnaire. Preoperative and postoperative scores were compared. RESULTS: Major advantages over other accepted implants are the stability of the implant, excellent biocompatibility, precise medialization with a micrometric screw, and ease of secondary adjustment. Mean VHI score in all handicap domains was significantly improved following vocal fold medialization (p < .01). Although no difference was found in regard to gender, younger patients had higher handicap scores than older patients preoperatively (p < .05). This difference was not present following medialization. CONCLUSIONS: The adjustable laryngeal implant offers many advantages over other techniques. Vocal cord medialization using the adjustable laryngeal implant significantly improves quality of life as measured by the VHI.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.051
GPT teacher head0.257
Teacher spread0.207 · 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 designNot applicable
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

Citations2
Published2008
Admission routes1
Has abstractyes

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