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Record W2184927429

Natural procreative technology for infertility and recurrent miscarriage

2012· article· en· W2184927429 on OpenAlexvenueno aff
Elizabeth Huiwen Tham, Karen C. Schliep, Joseph B. Stanford

Bibliographic record

VenueCanadian Family Physician · 2012
Typearticle
Languageen
FieldMedicine
TopicAssisted Reproductive Technology and Twin Pregnancy
Canadian institutionsnot available
Fundersnot available
KeywordsMiscarriageAssisted reproductive technologyMedicineIntrauterine inseminationObstetricsInfertilitySingletonGestationPregnancyReproductive technologyGynecologyArtificial inseminationProducts of conceptionLive birthBiology
DOInot available

Abstract

fetched live from OpenAlex

To study the outcomes of women with infertility or miscarriage treated with natural procreative technology (NaProTechnology or NPT), a systematic medical approach to promoting conception in vivo; and to compare the outcomes with those previously published from a general practice in Ireland. Results A total of 108 couples received NPT and were included in the analysis, of which 19 (18%) reported having 2 or more previously unexplained miscarriages. The average female age was 35.4 years. Couples had been attempting to conceive for a mean of 3.2 years. Twenty- two participants (20%) had previously given birth; 24 (22%) had previous intrauterine insemination; and 9 (8%) had previous assisted reproductive technology. The cumulative adjusted proportion of first live births for those completing up to 24 months of NPT treatment was 66 per 100 couples, and the crude proportion was 38%. The cumulative adjusted proportion of first conceptions was 73 per 100 couples, and the crude proportion was 47%. Of the 51 couples who conceived, 12 couples (24%) conceived with CrMS instruction alone, 35 (69%) conceived with CrMS and NPT medical treatment, and 4 (8%) conceived after additional surgical treatment. All births were singleton births; 54% were born at 37 weeks' gestation or later; and 78% had birth weights of 2500 g or greater.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.811
Threshold uncertainty score0.583

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)0.0000.000

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 head0.270
Teacher spread0.251 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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