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Record W3121609057 · doi:10.1097/grh.0000000000000012

Reproductive surgery in the 21st century

2018· article· en· W3121609057 on OpenAlexaff
Philippe R. Koninckx, Anastasia Ussia, L.V. Adamyan, Victor Gomel

Bibliographic record

VenueGlobal Reproductive Health · 2018
Typearticle
Languageen
FieldMedicine
TopicGynecological conditions and treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInfertilityAssisted reproductive technologyFecundityReproductive endocrinology and infertilityIn vitro fertilisationPregnancy rateLaparoscopyMedicineReproductive medicineReproductive technologyPregnancyObstetricsReproductionGynecologyGeneral surgeryPopulationBiology

Abstract

fetched live from OpenAlex

The result of infertility treatment can be assessed accurately by the monthly fecundity rate and the cumulative pregnancy rate (CPR). The monthly fecundity rate, decreasing over time, and the time needed to reach the ultimate CPR are key factors in decision making. Depending on the clinical assessment, infertility treatment will be either with in vitro fertilization (IVF)/assisted reproduction technologies (ART) or with a diagnostic laparoscopy associated with reproductive surgery, which thereafter my require require IVF/ART. The comparison of IVF/ART treatment versus reproductive surgery is therefore the wrong debate as the CPR’s of reproductive surgery and of IVF are additive. Decisions should be based on the ultimate CPR’s and on effort and time, not on personal preferences. The large majority of women with infertility should have a diagnostic laparoscopy during which reproductive surgery can be performed if needed. IVF/ART treatment without a diagnosis decreases the ultimate CPR and is not without potentially serious adverse effects. Having excellent reproductive surgery readily available to patients, similar to the availability of IVF would increase CPR in women with infertility and decrease the overall cost.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.164
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.050
GPT teacher head0.357
Teacher spread0.307 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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