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Record W4389027695 · doi:10.59350/5cx7q-5td27

Big Data in der Medizin

2014· preprint· de· W4389027695 on OpenAlexaboutno aff
Alexander Wragge

Bibliographic record

Venuenot available
Typepreprint
Languagede
FieldComputer Science
TopicMachine Learning in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

In Blutwerten, DNA-Profilen und Fallstudien könnten sich medizinisch nutzbare Erkenntnisse verbergen – wenn man sie im großen Stil erfasst und analysiert. Allerdings wirft "Big Data" in der Medizin auch Fragen auf – von der Rolle der Ärzte bis zum Datenschutz. Ein Forscherteam am Institute of Technology der University of Ontario überraschte 2010 die Fachwelt mit einer Beobachtung.

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)0.016
metaresearch head score (Gemma)0.046
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.019
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.046
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.008
Science and technology studies0.0020.007
Scholarly communication0.0130.020
Open science0.0020.009
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.0190.007

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 head0.136
GPT teacher head0.368
Teacher spread0.233 · 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
GenreCommentary

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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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