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Record W2067439875 · doi:10.1007/s00401-009-0612-2

Nomenclature and nosology for neuropathologic subtypes of frontotemporal lobar degeneration: an update

2009· article· en· W2067439875 on OpenAlexaff
Ian R. Mackenzie, Manuela Neumann, Eileen H. Bigio, Nigel J. Cairns, Irina Alafuzoff, Jillian J. Kril, Gábor G. Kovács, Bernardino Ghetti, Glenda M. Halliday, Ida E. Holm, Paul G. Ince, Wouter Kamphorst, Tamás Révész, Annemieke J.M. Rozemüller, Samir Kumar‐Singh, Haruhiko Akiyama, Atik Baborie, Salvatore Spina, Dennis W. Dickson, John Q. Trojanowski, David Mann

Bibliographic record

VenueActa Neuropathologica · 2009
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsFrontotemporal lobar degenerationNosologyNomenclaturePathologyDegeneration (medical)MedicineNeurosciencePsychologyBiologyFrontotemporal dementiaDementiaTaxonomy (biology)Disease

Abstract

fetched live from OpenAlex

Nomenclature and nosology for neuropathologic subtypes of frontotemporal lobar degeneration : an update

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)0.014
metaresearch head score (Gemma)0.019
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: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0080.006
Science and technology studies0.0020.003
Scholarly communication0.0040.008
Open science0.0080.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.002

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 head0.034
GPT teacher head0.314
Teacher spread0.280 · 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
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

Citations1,009
Published2009
Admission routes1
Has abstractyes

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