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Record W2022186573 · doi:10.1002/pd.2007

Postnatal management and long‐term outcome for survivors with congenital diaphragmatic hernia

2008· review· en· W2022186573 on OpenAlexaff
Priscilla Chiu, Holly L. Hedrick

Bibliographic record

VenuePrenatal Diagnosis · 2008
Typereview
Languageen
FieldMedicine
TopicCongenital Diaphragmatic Hernia Studies
Canadian institutionsSickKids FoundationUniversity of Toronto
Fundersnot available
KeywordsCongenital diaphragmatic herniaMedicineExtracorporeal membrane oxygenationPopulationHerniaDiaphragmatic breathingPediatricsSurvival rateIntensive care medicineFetusSurgeryPregnancyPathology

Abstract

fetched live from OpenAlex

Significant advances in the postnatal management of patients with congenital diaphragmatic hernia (CDH) have resulted in a remarkable improvement in survival rates over the past two decades. The success of current postnatal management of CDH patients has rendered fetal intervention to be limited to the most severe cases, and the role for prenatal treatment of CDH patients remains unclear. The adoption of lung-preserving strategies including high-frequency oscillatory ventilation (HFOV) and extracorporeal membrane oxygenation (ECMO) have improved CDH outcomes especially in those patients with significant ventilatory or circulatory compromise. Survival rates of up to 90% are being reported in some high-volume centers. However, the increased survival in CDH patients has been accompanied by an increase in neurological, nutritional and musculoskeletal morbidity among the long-term survivors. This has resulted in the need to provide resources for the long-term follow-up and support of this patient population. In this article, the postnatal management strategies and primary and secondary outcomes of high-volume international pediatric surgical centers will be reviewed. Finally, the role of a multidisciplinary management team for the follow-up of long-term CDH survivors will be discussed.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.851
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
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 head0.047
GPT teacher head0.327
Teacher spread0.280 · 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 designOther design
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

Citations102
Published2008
Admission routes1
Has abstractyes

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