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Record W4296554392 · doi:10.21203/rs.3.rs-2028498/v1

The impact of HIV on non-adherence for tamoxifen among women with breast cancer in South Africa

2022· preprint· en· W4296554392 on OpenAlexaff
Oluwatosin Ayeni, Shingirai Chiwambutsa, Wenlong Carl Chen, Nyasha Kapungu, Comfort Kanji, Roslyn Thelingwani, Nivashni Murugan, Rofhiwa Mathiba, Boitumelo Phakathi, Sarah Nietz, Duvern Ramiah, Daniel S. O’Neil, Judith S. Jacobson, Paul Ruff, Herbert Cubasch, Tobias Chirwa, Maureen Joffe, Collen Masimirembwa, Alfred I. Neugut

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsColumbia College
FundersNational Cancer InstituteMedical Research CouncilNational Institutes of HealthSouth African Medical Research Council
KeywordsTamoxifenBreast cancerOncologyMedicineHuman immunodeficiency virus (HIV)CancerGynecologyInternal medicineFamily medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.146
Threshold uncertainty score0.290

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.090
GPT teacher head0.434
Teacher spread0.344 · 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

Citations0
Published2022
Admission routes1
Has abstractno

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