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Record W2767089378 · doi:10.14740/jcgo.v6i3-4.458

Success of In Vitro Fertilization: A Researched Science or a Performance Indicator

2017· article· en· W2767089378 on OpenAlexvenueno aff
Mahmoud Salam

Bibliographic record

VenueJournal of Clinical Gynecology and Obstetrics · 2017
Typearticle
Languageen
FieldMedicine
TopicAssisted Reproductive Technology and Twin Pregnancy
Canadian institutionsnot available
Fundersnot available
KeywordsInfertilityReproductive medicineIn vitro fertilisationLive birthMedicineFemale infertilityFamily medicineGynecologyUnexplained infertilityPerceptionMale infertilityObstetricsPsychologyPregnancyBiology

Abstract

fetched live from OpenAlex

Divisions of reproductive medicine often perceive the live birth rate (LBR) as the single most important performance indicator for any infertility clinic, and it reflects on the quality of their services. Due to this perception, some infertility experts might refrain from disclosing these rates in publications as a researched science. Infertility experts might not be familiar with the various methods of reporting LBR as an outcome of in vitro fertilization (IVF) and therefore should be aware of these methods. Moreover, infertility experts might miss to take into account some couple and disease-related characteristics that could be successful determinants of this LBR. This is a brief review on infertility and its impact on married couples, as well as the history and description of IVF. We also present infertility experts with various methods of reporting LBR and shed light on some of the couple and disease-related factors associated with higher LBRs after IVF as reported in literature. J Clin Gynecol Obstet. 2017;6(3-4):53-57 doi: https://doi.org/10.14740/jcgo458w

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)0.097
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.097
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.003
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 head0.123
GPT teacher head0.451
Teacher spread0.328 · 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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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