Current management of type III and IV laryngotracheoesophageal clefts: the case for a revised cleft classification
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review summarizes the paediatric laryngotracheoesophageal cleft (LTEC) literature, with an emphasis on recent trends, evaluation and management, surgical techniques, postoperative care of Type III and IV LTECs, and to propose a revised cleft classification system that more accurately reflects our current understanding of these anomalies. RECENT FINDINGS: There are a number of techniques described to address Type III and IV LTEC, from endoscopic to open approaches with thoracotomy. The surgical approach should be tailored to the length of the cleft and its proximity to important anatomical structures. On the basis of review of the literature, we propose a modified Benjamin-Inglis classification (MBI) with subcategories to address this issue. Postoperative complications are common, namely, tracheoesophageal fistulae and tracheomalacia, which may necessitate subsequent procedures or prolonged tracheostomy dependence. SUMMARY: The medical and surgical management of Type III and IV LTEC is challenging with a high rate of morbidity and mortality. The rarity and difficulties in management of these malformations have made large cohort studies difficult, thus generalizable recommendations have been elusive. Experience and patient selection are critical for successful endoscopic repair. Anterior cervical approach, often with complete laryngofissure, appears to be the most common and preferred method for open repairs, though some use a lateral approach. The proposed MBI classification appears to be a useful adjunct to aid in surgical decision-making for deeper LTEC.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".