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Record W1606704944 · doi:10.4103/2249-4847.159867

Evaluating twins at risk for sepsis: The dilemma of the well-appearing co-twin

2015· article· en· W1606704944 on OpenAlexaff
V Shah, Eyad Almidani, Ann L Jefferies, Emad Khadawardi

Bibliographic record

VenueJournal of Clinical Neonatology · 2015
Typearticle
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineSepsisVital signsPediatricsRetrospective cohort studySigns and symptomsPresentation (obstetrics)Surgery

Abstract

fetched live from OpenAlex

Background: Early-onset sepsis is a significant cause of mortality and morbidity in neonates. Currently, there are no recommendations on how to manage a well-appearing co-twin if one twin meets the criteria for evaluation for sepsis. Objective: Review our experience on management of well-appearing co-twins when one twin (index case) is evaluated for sepsis. Methods: Retrospective review of twins born at ≥36 weeks gestation. Index cases were categorized into two groups: (1) Presence of clinical signs of sepsis and no risk factors (RFs), and (2) presence of clinical signs with RF. All co-twins were well-appearing. Clinical presentation, diagnosis, and management are presented for all twins. Results: The study included 33 twin pairs. Septic workup was performed in index cases due to the presence of clinical signs with no RF (66.7%) or clinical signs with RF (33.3%) and all received antibiotics. Septic workup was performed in 18.2% of co-twins when clinical signs but no RFs were present in the index cases and 36.4% of co-twins when clinical signs and RF were present. No cases of sepsis were identified in either twin. Conclusion: Variation in the management of a well-appearing co-twin exists when the other twin is evaluated due to clinical signs of sepsis with or without the presence of RF. Our small dataset suggests that it may be reasonable to defer a septic workup of the well-appearing co-twin; particularly in the absence of RF. A larger study is required to confirm this suggestion.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.206
GPT teacher head0.492
Teacher spread0.286 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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