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Record W4390906728 · doi:10.1038/s41582-024-00927-1

Cognitive criteria in HIV: greater consensus is needed

2024· review· en· W4390906728 on OpenAlexaff
Lucette A. Cysique, Bruce J. Brew, Jane Bruning, Desiree Byrd, Jane Costello, Kirstie Daken, Ronald J. Ellis, Pariya L. Fazeli, Karl Goodkin, Hetta Gouse, Robert K. Heaton, Scott Letendre, Jules Levin, Htein Linn Aung, Mónica Rivera Mindt, David J. Moore, Amy B. Mullens, Sérgio Monteiro de Almeida, José A. Muñoz-Moreno, C Motors As Applied To Power, Reuben N. Robbins, John Rule, Reena Rajasuriar, Micah J. Savin, Jeff Taylor, Mattia Trunfio, David E. Vance, Pui Li Wong, Steven Paul Woods, Edwina Wright, Sean B. Rourke

Bibliographic record

VenueNature Reviews Neurology · 2024
Typereview
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of TorontoUniversity of AlbertaSt. Michael's Hospital
FundersFogarty International Center
KeywordsHuman immunodeficiency virus (HIV)CognitionConsensus conferencePsychologyMedicinePsychiatryFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

A recently published Consensus Statement by Sam Nightingale and colleagues (Nightingale, S. et al. Cognitive impairment in people living with HIV: consensus recommendations for a new approach. Nat. Rev. Neurol . 19 , 424–433; 2023) 1 proposes a new approach to classifying cognitive impairment in people living with HIV. Although we applaud the efforts of the authors, other considerations are needed to ensure earlier and more consistent diagnosis, prevention and enhanced patient care. Furthermore, rather than rejecting the current criteria, a careful update of the HIV-associated neurocognitive disorder (HAND) criteria 2 would avoid an enormous historical loss resulting from new data becoming incomparable.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0030.002
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0040.002

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 head0.065
GPT teacher head0.401
Teacher spread0.336 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2024
Admission routes1
Has abstractno

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