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Record W2913235462

Incidence and Types of Congenital Anomalies in Newborns in Sulaimaniyah City in Iraq

2018· article· en· W2913235462 on OpenAlexaff
Niaz Mustafa Kamal, Nasih Othman

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldMedicine
TopicCongenital Anomalies and Fetal Surgery
Canadian institutionsYork University
Fundersnot available
KeywordsMedicineIncidence (geometry)Pediatrics
DOInot available

Abstract

fetched live from OpenAlex

Congenital anomalies or birth defects can be acquired during the fetal stages of development or from the genetic makeup of the parents. Congenital anomalies are important causes of infant and childhood illness and disability. Little is known about incidence and types of these anomalies in Iraqi Kurdistan. Therefore, this study was undertaken to estimate the incidence and types of congenital anomalies in Sulaimaniyah city. The study was carried out on the hospital's records of all newborns registered as having a congenital anomaly. The records of 586 neonates with congenital anomalies were analyzed from a total of 178,954 live broths that occurred during 4 years in the city. The data was obtained from the statistics section of maternal and a child unit of the Preventive Health Department. The overall incidence of all types of congenital anomalies over the four years was 3.3/1000 live births. There was a statistically significant difference in incidence between males and females over the four years, male to female risk ratio 1.2 (95% CI 1.02-1.42, P= 0.03). The commonest congenital anomalies affected the cardiovascular system accounting for 24% followed by those of the nervous system with 16%. Down syndrome accounted for 14% of all anomalies and cleft lip/palate for 11%. Types of anomalies were statistically associated with low birth weight and maternal age. The study indicates that the incidence of congenital anomalies is not high in the region; however, more extensive studies are required to give a more realistic incidence.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.183
GPT teacher head0.506
Teacher spread0.323 · 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 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

Citations7
Published2018
Admission routes1
Has abstractyes

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