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Record W3155254227 · doi:10.1002/hed.26702

Surgical, clinical, and functional outcomes of transoral robotic surgery used in sleep surgery for obstructive sleep apnea syndrome: A systematic review and meta‐analysis

2021· review· en· W3155254227 on OpenAlexaff
Jérôme R. Lechien, Carlos M. Chiesa‐Estomba, Nicolas Fakhry, Sven Saussez, Badr Ibrahim, Tareck Ayad, Younès Chekkoury‐Idrissi, Antoine E. Melkane, Ahmed Bahgat, Lise Crevier‐Buchman, M. Blumen, Giovanni Cammaroto, Claudio Vicini, Stéphane Hans

Bibliographic record

VenueHead & Neck · 2021
Typereview
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsObstructive sleep apneaMedicineEpworth Sleepiness ScaleMeta-analysisTransoral robotic surgeryCochrane LibrarySleep apneaApneaAnesthesiaSurgeryInternal medicinePolysomnography

Abstract

fetched live from OpenAlex

Abstract We investigated safety and efficacy of transoral robotic surgery (TORS) for base of tongue (BOT) reduction in obstructive sleep apnea syndrome (OSAS) patients. PubMed, Cochrane Library, and Scopus were searched. A meta‐analysis was performed. Random effects models were used. Thirty‐one cohorts met our criteria (1693 patients). The analysis was based mostly on retrospective studies. The summary estimate of the reduction of Apnea–Hypoxia Index (AHI) was 24.25 abnormal events per hour (95% CI: 21.69–26.81) and reduction of Epworth Sleepiness Scale (ESS) was 7.92 (95% CI: 6.50–9.34). The summary estimate of increase in lowest O 2 saturation was 6.04% (95% CI: 3.05–9.03). The success rate of TORS BOT reduction, either alone or combined with other procedures, was 69% (95% CI: 64–79). The majority of studies reported low level of evidence but suggested that TORS BOT reduction may be a safe procedure associated with improvement of AHI, ESS, and lowest O 2 saturation.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.774
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0340.008
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.181
GPT teacher head0.414
Teacher spread0.233 · 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.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations39
Published2021
Admission routes1
Has abstractyes

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