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Record W2906551649 · doi:10.4187/respcare.06106

Pulmonary Thromboses in Pediatric Acute Respiratory Distress Syndrome

2018· review· en· W2906551649 on OpenAlexaff
Magalie Caudron, Tanya Holt, Geoff D.E. Cuvelier, Jeffrey Dmytrowich, Gregory Hansen

Bibliographic record

VenueRespiratory Care · 2018
Typereview
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsCancerCare ManitobaUniversity of SaskatchewanUniversity of British Columbia
Fundersnot available
KeywordsMedicineARDSPulmonary embolismCoagulopathyIntensive care medicineRespiratory distressCardiologyInternal medicineLungAnesthesia

Abstract

fetched live from OpenAlex

Pediatric ARDS continues to be a management challenge in the ICU with prolonged hospitalizations and high mortality. Thromboembolic pulmonary embolism and in situ pulmonary artery thrombosis might represent underappreciated thrombotic processes for a subset of these patients. Although well described in the adult literature, descriptions of pulmonary thromboses with pediatric ARDS are limited to case reports. However, many risk factors for pulmonary thromboses are present in children with ARDS (eg, coagulopathy, endothelial injury, central venous catheters, concomitant inflammatory diseases), suggesting a much higher incidence is plausible. Based on an interpretation of animal, pediatric, and adult data, we propose a diagnostic algorithm to facilitate a timely and accurate diagnosis. Observing an alveolar dead space fraction ≥ 0.25, or either a 50% increase in physiologic dead space/tidal volume or a central venous saturation ≤ 60% over 24 h, triggers the algorithm. Together with targeted heparin treatment and right ventricular afterload reduction, clinical outcomes might improve if this particular patient subgroup can be identified early. While anticoagulation is recommended in adults with confirmed pulmonary embolism and low early mortality risk, data for children are limited.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.145
GPT teacher head0.407
Teacher spread0.262 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2018
Admission routes1
Has abstractyes

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