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Record W3001244877 · doi:10.1111/ppe.12607

The fragile foundations of the extended fetuses‐at‐risk approach

2020· article· en· W3001244877 on OpenAlexaff
Olga Basso

Bibliographic record

VenuePaediatric and Perinatal Epidemiology · 2020
Typearticle
Languageen
FieldMedicine
TopicAssisted Reproductive Technology and Twin Pregnancy
Canadian institutionsMcGill UniversityRoyal Victoria HospitalMcGill University Health CentreRoyal Victoria Regional Health Centre
Fundersnot available
KeywordsMedicineInterpretabilityFetusIncidence (geometry)Gestational agePregnancyObstetricsPediatrics

Abstract

fetched live from OpenAlex

BACKGROUND: Whether denominators for postnatal outcomes (ascertained after live birth) with a presumed prenatal origin should consist of fetuses or live births remains controversial. Proponents argue that the extended fetuses-at-risk (FAR) approach (a), provides a justification for medically indicated preterm delivery, (b), avoids paradoxical results, and (c), permits quantification of incidence of fetal-infant phenomena, such as "revealed" small for gestational age (SGA)-which, under FAR, rises with advancing gestation. METHODS: This conceptual paper examines the validity of the above arguments. RESULTS: As obstetricians induce babies early because of fetal (or maternal) compromise and despite the dangers posed by immaturity, there is no need to modify a paradigm that portrays preterm birth as a powerful risk factor. The FAR approach generally avoids "paradoxical" intersections because FAR rates of postnatal outcomes depend on the birth rate. However, this property, which causes rates of most postnatal outcomes to rise at term, can also lead to risk reversals and other misleading findings. The FAR formulation does not yield the incidence of postnatal conditions but, rather, the incidence of live birth (and survival to diagnosis) of babies with prevalent conditions (and, sometimes, future ones). CONCLUSIONS: The proposed arguments do not provide adequate support for extending the FAR approach to postnatal outcomes. As only live births can contribute to the numerator of rates, the usefulness and interpretability of FAR measures in this setting are limited.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.098
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.001
Science and technology studies0.0020.025
Scholarly communication0.0040.010
Open science0.0020.005
Research integrity0.0020.007
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 head0.036
GPT teacher head0.290
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations5
Published2020
Admission routes1
Has abstractyes

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