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Maternal Cell Contamination of Amniotic Fluid Samples Obtained by Open Needle Versus Trocar Technique of Amniocentesis

2002· article· en· W166827699 on OpenAlexafffundvenueabout
Helen Steed, Darrell J. Tomkins, Doug R. Wilson, Nanette Okun, Damon C. Mayes

Bibliographic record

VenueJournal of Obstetrics and Gynaecology Canada · 2002
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsUniversity of British ColumbiaStollery Children's HospitalUniversity of Alberta
FundersUniversity of Alberta
KeywordsAmniocentesisMedicineAmniotic fluidObstetricsBloodyIncidence (geometry)SurgeryPregnancyPrenatal diagnosisFetus

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the incidence of maternal cell contamination (MCC) in the open-needle amniocentesis sampling technique compared with the trocar-in-place technique. METHODS: A retrospective analysis was conducted on 2,498 mid-trimester amniocenteses performed in two tertiary care centres in Canada. The University of Alberta centre used the open-needle (without the trocar) technique and the University of British Columbia centre used the standard (with the trocar in place) technique. Data were gathered regarding the nature of the amniotic fluid, number of needle passes, amniocentesis results, and the occurrence of maternal cell contamination. The statistical analysis used logistic regression, and controlled for the potential confounders of bloody fluid taps and requirement for more than one needle insertion. RESULTS: The incidence of maternal cell contamination was 1.16% with the open-needle technique and 0.78% with the standard trocar-in-place technique (p < 0.315), with a power of 42%. CONCLUSION: The data suggested there is no significant increase in maternal cell contamination with the open-needle versus trocar-in-place techniques of amniocentesis. However, the small sample size, combined with the low prevalence of the outcome of interest (MCC), provides insufficient power to draw firm conclusions about the difference in MCC between the two techniques.

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)0.003
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 score0.103
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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

Citations11
Published2002
Admission routes4
Has abstractyes

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Same venueJournal of Obstetrics and Gynaecology CanadaSame topicPrenatal Screening and DiagnosticsFrench-language works237,207