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Epidemiology and Public Health

2024· paratext· en· W2471468265 on OpenAlexaboutno aff
Keith Alcorn

Bibliographic record

Venuenot available
Typeparatext
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTransmission (telecommunications)CondomPublic healthEpidemiologyHuman immunodeficiency virus (HIV)Female circumcisionDiseaseDisease controlEnvironmental healthFamily medicineImmunologyGynecologyInternal medicinePathology

Abstract

fetched live from OpenAlex

There has been much concern over whether women are treated equally to men with respect to HIV/AIDS research. At the Vancouver Conference, it was evident that progress toward this goal has been shamefully slow. Whether protease inhibitors work as well in women with liver disease as they do in men has not been studied. In most parts of the developed world, a high proportion of HIV-infected women have histories of drug use, and possibly serious liver damage. When potential problem areas are brought to light, such as increased risk for genital cancers in women, suggestions for treatment are not made. In some areas, self help groups and advocacy organizations exist, but more need to be developed to help women cope with HIV infection, and to help them become educated to maintain their own health and prevent infection. Methods giving women some control over HIV prevention include the female condom and microbiocides, with the former being potentially effective but less accepted by male partners in some countries. Attention was also given to the female aspect of HIV transmission, such as viral shedding in genital secretions. Finally, the known effect of AZT on preventing perinatal transmission of HIV was discussed.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.934
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0660.010

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 head0.165
GPT teacher head0.480
Teacher spread0.315 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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