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Record W2053428721 · doi:10.1055/s-0028-1103512

Association between Hematologic Findings and Brain Injury due to Neonatal Hypoxic-Ischemic Encephalopathy

2008· article· en· W2053428721 on OpenAlexaff
Vibhuti Shah, Joseph Beyene, Prakesh S. Shah, Max Perlman

Bibliographic record

VenueAmerican Journal of Perinatology · 2008
Typearticle
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsHospital for Sick ChildrenMount Sinai Hospital
Fundersnot available
KeywordsMedicineEncephalopathyAdverse effectPlateletAnesthesiaPediatricsInternal medicinePhysiology

Abstract

fetched live from OpenAlex

We sought to analyze associations between aberrant hematologic counts and adverse outcome in neonates with hypoxic ischemic encephalopathy (HIE) due to various intrapartum asphyxial insult types and to describe the postnatal changes in counts. Hematologic counts between 0 and 120 hours of age of 316 consecutively admitted neonates with HIE were collected retrospectively. Asphyxial insult types were categorized as acute near-total, prolonged partial, or "mixed." Associations between hematologic counts and adverse outcome by 2 years of age were analyzed. No associations were found between patterns of hematologic counts in the first 12 hours and adverse outcome. Lymphocyte counts peaked in the first 2 hours of age in all insult types (46% had values > 10.0 x 10 (9)/L) and by 4 to 6 hours reached normal levels. Nucleated red blood cell counts peaked between 6 and 8 hours of age and fell to normal levels by 36 to 72 hours of age (56% had values > 1860 x 10 (6)/L). Relatively few subjects had low platelet counts by age 12 hours; the nadir occurred on days 2 to 3. No associations were found between hematologic counts and adverse outcome. The characteristic hematologic changes found are attributable to the asphyxial insults, not to brain injury. Because of inconsistent changes, hematologic counts cannot be used on their own to time asphyxial insults.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.263
Teacher spread0.252 · 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

Citations14
Published2008
Admission routes1
Has abstractyes

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