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Record W2999842307 · doi:10.29173/hsi200

Ghosts of Gene Therapies Past- Lessons Learned From Jesse Gelsinger

2019· article· en· W2999842307 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueHealth Science Inquiry · 2019
Typearticle
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsMcGill University
Fundersnot available
KeywordsHistory

Abstract

fetched live from OpenAlex

Prior to the complete sequencing of the human genome, the development of induced pluripotent stem cells and the fanfare surrounding Clustered Regularly Interspaced Short Palendromic Sequences (CRISPR) genetic editing systems, the biomedical sciences had an even bigger preeminent heavyweight: gene therapy. The development of techniques to sequence, clone, and directionally insert DNA in vitro collided at the inevitable junction of this revolutionary new science. However, bringing gene therapy applications to clinical trials revealed dangers and pitfalls that still hinder the field today. The tragic death of Jesse Gelsinger continues to highlight the multitude of hazards that have been linked to gene therapy for over 20 years, as well as the ethical considerations regarding the conflicts of interest present in clinical science.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.109
GPT teacher head0.392
Teacher spread0.283 · 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