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Record W1981183498 · doi:10.1155/2012/814564

Short-Term Rationing of Combination Antiretroviral Therapy: Impact on Morbidity, Mortality, and Loss to Follow-Up in a Large HIV Treatment Program in Western Kenya

2012· article· en· W1981183498 on OpenAlexaff
April J. Bell, Kara Wools‐Kaloustian, Sylvester Kimaiyo, Hai Liu, Adrian Katschke, Changyu Shen, Gilbert Simiyu, Beverly Musick, John E. Sidle, Abraham Siika, Paula Braitstein

Bibliographic record

VenueAIDS Research and Treatment · 2012
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Toronto
FundersU.S. President’s Emergency Plan for AIDS ReliefRockefeller FoundationUnited States Agency for International Development
KeywordsAlgorithmMedicineHuman immunodeficiency virus (HIV)CartMachine learningMathematicsComputer scienceFamily medicineHistoryArchaeology

Abstract

fetched live from OpenAlex

Background. There was a 6-month shortage of antiretrovirals (cART) in Kenya. Methods. We assessed morbidity, mortality, and loss to follow-up (LTFU) in this retrospective analysis of adults who were enrolled during the six-month period with restricted cART (cap) or the six months prior (pre-cap) and eligible for cART at enrollment by the pre-cap standard. Cox models were used to adjust for potential confounders. Results. 9009 adults were eligible for analysis: 4,714 pre-cap and 4,295 during the cap. Median number of days from enrollment to cART initiation was 42 pre-cap and 56 for the cap (P < 0.001). After adjustment, individuals in the cap were at higher risk of mortality (HR = 1.21; 95% CI : 1.06-1.39) and LTFU (HR = 1.12; 95% CI : 1.04-1.22). There was no difference between the groups in their risk of developing a new AIDS-defining illness (HR = 0.92 95% CI 0.82-1.03). Conclusions. Rationing of cART, even for a relatively short period of six months, led to clinically adverse outcomes.

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)0.000
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 score0.110
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.129
GPT teacher head0.471
Teacher spread0.342 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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