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Record W2395090910

Medialization thyroplasty for unilateral vocal cord paralysis secondary to advanced extralaryngeal malignant disease: review of operative morbidity and patient life expectancy.

2012· article· en· W2395090910 on OpenAlexaff
Andrew T Morrissey, Daniel A. O’Connell, Michael Allegretto

Bibliographic record

VenuePubMed · 2012
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsUniversity of Alberta HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineSurgeryMalignancyLung cancerParalysisRetrospective cohort studyVocal cord paralysisCordCohortInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To review the operative morbidity and the overall length of survival following medialization thyroplasty for unilateral vocal cord paralysis (UVCP) due to advanced extralaryngeal malignancy. DESIGN: Retrospective review. SETTING: Tertiary care laryngology practice. METHOD: All cases of medialization thyroplasty over a 3-year period were reviewed. Only patients who had UVCP due to advanced extralaryngeal malignancy were included. Any cases from iatrogenic causes or for any other reason were excluded. Survival days were calculated from the date of the thyroplasty. MAIN OUTCOME MEASURE: Survival days postmedialization thyroplasty. RESULTS: Twenty-one patients met the inclusion criteria. Two distinct groups within this cohort were identified: (1) those suffering from advanced lung cancer and (2) those with metastatic cancer of another origin (ie, breast, renal cell, esophageal). There were 11 patients in the lung cancer group and 10 in the other group. Average survival was 538 days in the lung cancer group and 668 days in the other group. The procedure was well tolerated, with only one postoperative complication, which was a minor wound infection. CONCLUSION: For patients suffering from advanced malignancy, medialization thyroplasty is a safe procedure and an excellent modality for voice palliation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.513

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 head0.024
GPT teacher head0.285
Teacher spread0.261 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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