Using attenuation coefficients from optical coherence tomography as markers of vocal fold maturation
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Optical coherence tomography (OCT) is a promising technology to noninvasively assess vocal fold microanatomy. The goal of this study was to develop a methodology using OCT to identify quantifiable markers of vocal fold development. STUDY DESIGN: In vivo study. METHODS: A two-step process was developed to reproducibly image the midmembranous vocal fold edge of 10 patients younger than 2 years and 10 patients between 11 and 16 years of age using OCT. An image analysis algorithm was implemented to extract OCT-derived A-lines for each patient. These A-lines were divided into three zones according to apparent changes in slope. Relative attenuation coefficients, or tissue- and system-dependent parameters that describe the rate at which optical signal decays, were calculated for each zone. RESULTS: Young patients had distinct relative attenuation coefficients in zone 1 (P < .0001), whereas zones 2 and 3 were indistinct (P = .1129). Older patients had distinct relative attenuation coefficients in zones 1, 2, and 3 (P < .0370). Between age groups, relative attenuation coefficients were different in zones 2 and 3 (P < .0001, P = .0315, respectively) and indistinct in zone 1 (P = .1438). CONCLUSIONS: Relative attenuation coefficients can be used as markers of vocal fold development. Differences in relative attenuation coefficients likely represent changes in extracellular matrix structure within the lamina propria and may become useful for guiding treatment of voice disorders in the pediatric population. LEVEL OF EVIDENCE: NA Laryngoscope, 126:E218-E223, 2016.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".