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Record W2785428848 · doi:10.1352/1934-9556-56.1.1

Reproductive Cancer Treatment Hospitalizations of U.S. Women With Intellectual and Developmental Disabilities

2018· article· en· W2785428848 on OpenAlexaff
Susan L. Parish, Esther Son, Robyn M. Powell, Leah Igdalsky

Bibliographic record

VenueIntellectual and developmental disabilities · 2018
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsHealth Sciences North
Fundersnot available
KeywordsIntellectual disabilityMedicinePsychological interventionCancerHealthcare Cost and Utilization ProjectPublic healthCervixCancer registryGynecologyGerontologyFamily medicineHealth carePsychiatryNursingInternal medicine

Abstract

fetched live from OpenAlex

There is a dearth of existing research on the treatment of reproductive cancers among women with intellectual and developmental disabilities (IDD). This study analyzed the 2010 Healthcare Cost and Utilization Project Nationwide Inpatient Sample and compared the prevalence of reproductive cancer treatment hospitalization discharges among women with and without IDD. Discharges linked to women with IDD had higher incidences of cancer of the uterus and lower prevalence of cancer of the cervix. Moreover, discharges linked to women with IDD indicated these women were younger, had longer hospital stays, and were more likely to have public insurance coverage. Therefore, further research and targeted interventions to increase cancer prevention and screening are urgently needed.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.265
Teacher spread0.240 · 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 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

Citations11
Published2018
Admission routes1
Has abstractyes

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