Predicting Maximal Costal Cartilage Graft Size for Laryngotracheal Reconstruction
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Current methods of assessing rib cartilage dimensions for laryngotracheal reconstruction (LTR) are inexact, making surgical planning difficult. The purpose of this study was to determine the most appropriate rib for costal cartilage graft LTR to minimize the number of ribs harvested and improve surgical outcomes. STUDY DESIGN: Retrospective review. METHODS: Computed tomography imaging of chest scans in 25 children aged 1 to 18 years was evaluated. The lengths and widths of medial and lateral cartilaginous segments of ribs 4 to 7 were measured bilaterally. Right and left cartilaginous rib dimensions were compared using a two-sample t-test. Linear mixed-effect regression was performed to develop models quantifying the relationship between rib size and patient height, rib side, and rib number. RESULTS: = 0.71, 0.77, respectively). There was no difference in rib dimensions across chest sides. Rib length and width increased with child height. Total cartilaginous rib length increased from superiorly to inferiorly, primarily due to an increase in the dimensions of the medial portion of each rib. CONCLUSION: Cartilaginous rib lengths and widths were associated with patient height, with taller children having longer ribs. Inferior ribs were longer than superior ribs, suggesting that inferior ribs may be preferred for LTR. There was no difference in cartilaginous rib length across chest side. Results may help surgeons with preoperative planning. LEVEL OF EVIDENCE: NA Laryngoscope, 132:1682-1686, 2022.
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 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 | 0.003 | 0.018 |
| 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) | 0.004 | 0.001 |
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".