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Record W1870437875 · doi:10.1055/s-0038-1627055

Discussion of “Attitude of Physi -cians Towards Automatic Alerting in Computerized Physician Order Entry Systems”

2013· article· en· W1870437875 on OpenAlexaff
David W. Bates, Melissa Baysari, Martin Dugas, Walter E. Haefeli, André Kushniruk, Christoph U. Lehmann, J. Liu, J. Mantas, Álvaro Margolis, Kengo Miyo, Christian Nøhr, Mor Peleg, Fernán González Bernaldo de Quirós, Sarah P. Slight, Jack Starmer, Katsuhiko Takabayashi, Johanna Westbrook

Bibliographic record

VenueMethods of Information in Medicine · 2013
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputerized physician order entryOrder (exchange)Order entryMedicineOperations researchAeronauticsEngineeringPolitical scienceLawMedical emergencyHealth careBusiness

Abstract

fetched live from OpenAlex

With these comments on the paper "Attitude of Physicians Towards Automatic Alerting in Computerized Physician Order Entry Systems", written by Martin Jung and co-authors, with Dr. Elske Ammenwerth as senior author [1], the journal wants to stimulate a broad discussion on computerized physician order entry systems. An international group of experts have been invited by the editor of Methods to comment on this paper. Each of the invited commentaries forms one section of this paper.

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)0.046
metaresearch head score (Gemma)0.225
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 score0.046
Threshold uncertainty score0.245

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.225
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0050.007
Scholarly communication0.0050.005
Open science0.0050.004
Research integrity0.0230.021
Insufficient payload (model declined to judge)0.0100.003

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 head0.048
GPT teacher head0.474
Teacher spread0.426 · 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".

Quick stats

Citations13
Published2013
Admission routes1
Has abstractyes

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