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Record W2408696369 · doi:10.1177/0194599816650637

Mobile Endoscopy vs Video Tower

2016· article· en· W2408696369 on OpenAlexaff
Hao Liu, Salwa Akiki, Nicholas Barrowman, Matthew Bromwich

Bibliographic record

VenueOtolaryngology · 2016
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsCLIPSSignificant differenceTowerVideo qualityComputer scienceMobile deviceMedicineMultimediaArtificial intelligenceEngineeringInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine if any significant difference exists between endoscopic videos captured with a mobile adaptor and videos captured with a traditional tower. STUDY DESIGN: Prospective controlled blinded comparison of mobile endoscopic videos captured through 2 methods. METHODS: Thirty randomly selected patients underwent video endoscopy with both mobile and video tower recording methods. Sixty videos were edited into a series of 10-second clips. Thirteen otolaryngology staff and residents rated the video quality and provided a diagnosis for each video. RESULTS: We found no significant difference in the video quality ratings between mobile and tower videos (mean difference, -0.07; P < .37). Similarly, we found no significant difference in the observers' diagnostic accuracy (mean difference, 1.54%; P < .686). CONCLUSION: With adequate power, our study was unable to demonstrate a difference between mobile adapter videos and tower videos. Our findings suggest that mobile adapter videos may reasonably be used in lieu of tower videos in clinical practice.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score0.999

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)0.0070.002

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 head0.010
GPT teacher head0.258
Teacher spread0.248 · 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; both teacher heads agree on what is shown here.

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

Citations22
Published2016
Admission routes1
Has abstractyes

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