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Paediatric airway stenting: An endoscopic approach to the management of severe airway obstruction

2025· article· en· W7117711683 on OpenAlexaff
Tejaswi Chandra, Manoj Madhusudan, Priyanka Potti, Kaustubh Mohite, J. T. Srikanta

Bibliographic record

VenueLung India · 2025
Typearticle
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsASTER
Fundersnot available
KeywordsAirway obstructionAirwayAirway managementTherapeutic approachMEDLINE

Abstract

fetched live from OpenAlex

BACKGROUND: Airway stenting is a complex bronchoscopic intervention that is infrequently performed in children due to limited training opportunities and a lack of appropriate paediatric equipment. We present our institutional data on children (<18 years) who underwent airway stent placement for various tracheobronchial pathologies. METHODS: We conducted a retrospective study of children with notable airway anomalies who underwent stent placement at our institute over a 3.5-year period (January 1, 2020, to June 30, 2023). Data collected included patient age, type and severity of airway obstruction, clinical condition at presentation, type of stent used, complications, outcomes, and follow-up findings. RESULTS: During the study period, 13 stents were deployed in 12 children with tracheobronchial obstruction. Indications for stenting included severe airway stenosis, airway malacia, stenosis with malacia, and tracheoesophageal fistula (TEF). Stent selection depended on age, lesion location, and device availability. No procedure-related mortalities were noted. The most common complication was granulation tissue formation. After an average 10-month follow-up, 85.7% were clinically stable following stent removal. CONCLUSION: Although limited by sample size, our findings support the safety and efficacy of airway stenting in children with tracheobronchial obstruction. Stenting serves as a suitable therapeutic choice when conventional approaches have been unsuccessful or are not suitable. Therefore, it is crucial to establish a robust follow-up system to promptly address any potential complications.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.250
Teacher spread0.241 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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