MétaCan
Menu
Back to cohort
Record W2156527970 · doi:10.1017/s0022215113001825

Accuracy of flexible versus rigid laryngoscopic photo-documentation in the diagnosis of early glottic cancer

2013· article· en· W2156527970 on OpenAlexaff
Faisal Makki, Ahidul Hilal, Elaine Fung, R. Hart, S. Mark Taylor, Timothy S. Brown

Bibliographic record

VenueThe Journal of Laryngology & Otology · 2013
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLaryngoscopesMedicineWorksheetGlottisLaryngoscopyMedical physicsSurgeryRadiologyLarynxPsychologyIntubation

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the image quality provided by rigid laryngoscopes versus flexible distal-chip laryngoscopes when documenting the same laryngeal pathology. METHODS: This paper reports a prospective single-blind study. Ten early stage glottic cancer cases were selected. Photographs of the pathologies were taken using both rigid and flexible distal-chip laryngoscopes (a total of 20 photographs). Nineteen clinicians were asked to review the laryngoscopic photographs; the clinicians were provided with a worksheet, which included questions regarding the clinical description, photograph quality and overall satisfaction with the images obtained. Clinicians’ responses to the worksheet questions were then analysed. RESULTS: The overall accuracy rate for lesion sidedness, anatomical sub-site involvement, anterior commissure involvement and tumour staging were 94.7 per cent, 46.6 per cent, 53.7 per cent and 47.1 per cent respectively. There were no statistically significant differences in terms of the accuracy rates, photograph quality or overall satisfaction with the photographs obtained by either modality. CONCLUSION: There were no statistically significant differences demonstrated in overall clinical accuracy or perceived image quality between the use of the rigid or flexible endoscopes when interpreting images of early glottic cancer.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.356
Teacher spread0.323 · 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 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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Laryngology & OtologySame topicDigital Imaging in MedicineFrench-language works237,207