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Record W3201542632 · doi:10.29309/tpmj/2018.25.08.61

ASPHYXIA NEONATORUM;

2018· article· en· W3201542632 on OpenAlexaff
Iqbal Ahmed, Umair Arshad, Hafiz Muhammad Anwar ul Haq, Sobia Tabassum, Arshia Sabir, Hafiz Muhammad Ejaz ul Haq

Bibliographic record

VenueThe Professional Medical Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsDeer Lodge Centre
Fundersnot available
KeywordsAsphyxia NeonatorumMedicineAsphyxiaApgar scoreBirth weightPerinatal asphyxiaPediatricsLow birth weightAnesthesiaObstetricsPregnancy

Abstract

fetched live from OpenAlex

Introduction: Severe hypoxic ischemic organ damage is caused by asphyxia innewborns which can follow fatal outcomes or severe life-long pathologies like renal insufficiency.We wanted to note the frequency of renal derangement in neonates having asphyxia neonatoumin this study. Setting & Period: Department of Pediatrics, Bahawal Victoria Hospital (BVH),Bahawalpur, from 1st January 2017 to 31st June 2017. Materials & Methods: Two hundredand sixty four neonates of both genders with birth asphyxia were included in the study. Mainoutcome was renal derangement in asphyxia neonatorum. Results: Mean weight was 2.54kg with standard deviation 0.50 kg and having mean APGAR score 4.43 with SD 1.66. 0It wasnoted that 189 (71.6%) neonates had Renal derangement in which 109 (57.7%) were males and80 (42.3%) were females with mean of weight was 2.53kg, having mean APGAR score 4.44.Conclusion: Renal derangement is quite common in neonates with birth asphyxia.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0040.000

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 head0.016
GPT teacher head0.329
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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