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Record W3081015616 · doi:10.1186/s12887-020-02295-3

Gender and birth weight as risk factors for anastomotic stricture after esophageal atresia repair: a systematic review and meta-analysis

2020· review· en· W3081015616 on OpenAlexaboutno aff
Anahid Teimourian, Felipe Donoso, Pernilla Stenström, Helena Arnadottir, Einar Arnbjörnsson, Heléne Engstrand Lilja, Martin Salö

Bibliographic record

VenueBMC Pediatrics · 2020
Typereview
Languageen
FieldMedicine
TopicEsophageal and GI Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisAnastomosisAtresiaEsophageal strictureLow birth weightSurgeryGeneral surgeryInternal medicineEsophagusPregnancy

Abstract

fetched live from OpenAlex

BACKGROUND: Anastomotic stricture (AS) is the most frequently occurring complication that occurs after esophageal atresia (EA) repair. Nevertheless, the pathogenesis remains primarily unknown and there is inadequate knowledge regarding the risk factors for AS. Therefore, a systematic review of the literature and a meta-analysis was performed to investigate whether gender and birth weight were risk factors for the development of AS following EA repair. METHODS: The main outcome measure was the occurrence of AS. Forest plots with odds ratios (OR) and 95% confidence intervals (CI) were generated for the outcomes. Quality assessment was performed using the Newcastle-Ottawa scale. RESULTS: Six studies with a total of 495 patients were included; 59% males, and 37 and 63% of the patients weighed < 2500 g and ≥ 2500 g, respectively. Male gender (OR, 0.96; 95% CI, 0.66-1.40; p = 0.82) and birth weight < 2500 g (OR, 0.74; 95% CI, 0.47-1.15; p = 0.18) did not increase the risk of AS. The majority of the included studies were retrospective cohort studies and the overall risk of bias was considered to be low to moderate. CONCLUSION: Neither gender nor birth weight appear to have an impact on the risk of AS development following EA repair.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0120.005
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.058
GPT teacher head0.325
Teacher spread0.266 · 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 designMeta-analysis
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

Citations17
Published2020
Admission routes1
Has abstractyes

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