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Record W4386156572 · doi:10.1097/cce.0000000000000969

Pediatric Acute Respiratory Distress Syndrome and Tracheal Injury in a Patient Requiring Extracorporeal Membrane Oxygenation Following Cement Aspiration: A Case Report

2023· article· en· W4386156572 on OpenAlexaff
Madeleine Böhrer, Long Cai, Adrienne Thompson, Stasa Veroukis, Gurpreet Khaira

Bibliographic record

VenueCritical Care Explorations · 2023
Typearticle
Languageen
FieldMedicine
TopicEsophageal and GI Pathology
Canadian institutionsChildren's Hospital of WinnipegChildren's Hospital Research Institute of ManitobaUniversity of AlbertaStollery Children's Hospital
Fundersnot available
KeywordsMedicineARDSExtracorporeal membrane oxygenationPneumomediastinumRespiratory distressAnesthesiaAspiration pneumoniaBronchoscopyBronchoalveolar lavageAirwayRespiratory failureIntubationSurgeryPneumoniaLungComplicationInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Ingestion and aspiration of caustic substances is a common problem in pediatrics and carries the risk of associated aspiration pneumonitis, laryngeal injury, and esophageal injury. Extracorporeal membrane oxygenation (ECMO) has been used to support adults with acute respiratory distress syndrome (ARDS) from aspiration of cement dust, however, literature outlining pediatric management in cases of alkali lung and airway injuries is lacking. CASE SUMMARY: A 6-year-old boy presented with ARDS from cement aspiration requiring high-pressure ventilation. He had further complications of tracheal injury with subsequent pneumomediastinum secondary to the alkali burn. He required ECMO to facilitate repeat bronchoscopy for cement particle washout and to enable recovery from ARDS and tracheal injury. CONCLUSION: This case highlights the need to perform early bronchoscopy and gastrointestinal endoscopy for injury assessment and foreign body removal in alkali burns. It also emphasizes the value of ECMO support for respiratory failure and facilitating bronchoalveolar lavage when it is not otherwise tolerated.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.053
GPT teacher head0.354
Teacher spread0.302 · 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 designCase report
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
Published2023
Admission routes1
Has abstractyes

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