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Record W3112249703 · doi:10.5489/cuaj.6824

Infertility insurance: What coverage exists for physician trainees?

2020· article· en· W3112249703 on OpenAlexvenueaboutno aff
Wade Muncey, Erin Jesse, Aram Loeb, Nannan Thirumavalavan

Bibliographic record

VenueCanadian Urological Association Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsInfertilityMale infertilityFamily medicinePopulationMedicineHealth careGynecologyPolitical scienceEnvironmental healthPregnancyBiology

Abstract

fetched live from OpenAlex

INTRODUCTION: We aimed to describe infertility insurance coverage provided to male and female fellows working at institutions that offer advanced infertility training. METHODS: Faculty and fellows working within U.S. and Canadian andrology or reproductive endocrinology and infertility (REI) programs were contacted and asked for a copy of their institutional health insurance summary of benefits. Documents were assessed for coverage of diagnosis and treatment, shared costs, and maximum lifetime coverage for infertility care. RESULTS: Insurance policies from 24 institutions were reviewed; 16 of 24 (66%) institutions covered costs related to the diagnosis of infertility. Six institutions (25%) offered coverage for diagnosis but not treatment. There were 15 (62.5%) institutions that offered some amount of coverage for the treatment of infertility, and the average lifetime maximum was $16 100. Only six of 24 (25%) plans explicitly described a covered male-specific treatment, which included sperm extraction (12.5%), varicocele repair (4.2%), and sperm cryopreservation (8.3%). CONCLUSIONS: For physician trainees, infertility insurance coverage is not universal, policies are not transparent, and treatment for male factor infertility is often omitted. With high costs of infertility treatment, variable insurance coverage, and debt and time constraints, residents and fellows are a particularly vulnerable population that may experience significant financial toxicity when faced with infertility.

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)0.004
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 score0.208
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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 integrity0.0000.001
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 head0.034
GPT teacher head0.277
Teacher spread0.243 · 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
Published2020
Admission routes2
Has abstractyes

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