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Record W2187490615

MATERNAL-FETAL MEDICINE COMMITTEE MEMBERS

2000· article· en· W2187490615 on OpenAlexaboutno aff
Joan Crane, Nancy Kent, Gregory J. Reid, John Van Aerde, Edmonton Ab, Douglas Bell, D P Davies, Guy Hogan, Ken Milne, Vyta Senikas, Harold Wiens

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicAutopsy Techniques and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMEDLINETest (biology)Psychological interventionCochrane LibraryFamily medicineRandomized controlled trialNursingSurgery
DOInot available

Abstract

fetched live from OpenAlex

Objective: to design national guidelines instructing obstetric care providers when, and in what populations, to consider antenatal fetal testing; which testing options are available; when to choose one testing method over another; and the expected impact on perinatal morbidity and mortality. Options: clinical situations associated with an increased risk of fetal asphyxia. Outcomes: perinatal morbidity and mortality. Evidence: Medline search from 1966 to 2000 for English language articles related to: methods of antenatal testing; comparisons of antenatal testing modalities; and impact of antenatal testing methods on perinatal morbidity and mortality. A review of meta-analyses related to antenatal testing found in the Cochrane Collaboration. Values: the evidence collected was reviewed by the MaternalFetal Medicine Committee of the SOGC under the leadership of the primary author and quantified using the evaluation of evidence guidelines developed by the Canadian Task Force on the Periodic Health Exam. Benefits, harms and costs: antenatal testing in defined populations at risk for fetal asphyxia has been shown to decrease perinatal morbidity and mortality. False positive test results can be reduced by employing a hierarchy of antenatal testing methods, reducing unnecessary intervention. Cost/benefit

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.381
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0900.000

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 head0.015
GPT teacher head0.299
Teacher spread0.283 · 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 designNot applicable
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

Citations0
Published2000
Admission routes1
Has abstractyes

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