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Record W4403259996 · doi:10.1016/s0140-6736(24)01121-8

The Lancet Commission on self-harm

2024· review· en· W4403259996 on OpenAlexaff
Paul Moran, Amy Chandler, Pat Dudgeon, Olivia J Kirtley, Duleeka Knipe, Jane Pirkis, Mark Sinyor, Rosie Allister, Jeffrey Ansloos, M. Ball, Lai Fong Chan, Leilani Darwin, Kate Derry, Keith Hawton, Veronica Heney, Sarah Hetrick, Ang Li, Daiane Borges Machado, Emma McAllister, David McDaid, Ishita Mehra, Thomas Niederkrotenthaler, Matthew K. Nock, Victoria M. O’Keefe, María A. Oquendo, Joseph Osafo, Vikram Patel, Soumitra Pathare, Shanna Peltier, Tessa Roberts, Jo Robinson, Fiona Shand, Fiona J. Stirling, Jon Petter Stoor, Natasha Swingler, Gustavo Turecki, Svetha Venkatesh, Waikaremoana Waitoki, Michael Wright, Paul S. F. Yip, Michael J. Spoelma, Navneet Kapur, Rory C. O’Connor, Helen Christensen

Bibliographic record

VenueThe Lancet · 2024
Typereview
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsMcGill UniversityHealth Sciences CentreInstitute for Christian StudiesUniversity of TorontoSunnybrook Health Science Centre
FundersEconomic and Social Research CouncilUniversity of BristolNational Institute for Health and Care ResearchNational Institute for Health and Care Excellence
KeywordsCommissionHarmDo no harmMedicinePolitical scienceLawPsychiatry

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)0.018
metaresearch head score (Gemma)0.055
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.045
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.055
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0090.008
Science and technology studies0.0010.003
Scholarly communication0.0080.004
Open science0.0030.005
Research integrity0.0150.013
Insufficient payload (model declined to judge)0.0450.018

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 head0.168
GPT teacher head0.433
Teacher spread0.265 · 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

Citations95
Published2024
Admission routes1
Has abstractno

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