MétaCan
Menu
Back to cohort
Record W4254903350 · doi:10.3233/thc-2000-83-406

Scientific Papers

2000· article· en· W4254903350 on OpenAlexafffund
R Benardon, Andreas Lüttgau, M. A. Keller-Reichenbecher, Wolfgang Schlegel, Rolf Bendl, Benoı̂t Thirion, Stéfan Darmoni, J Leroy, Magaly Douyère, Josette Piot, Christos Ilioudis, G. Pangalos, Kevin De Boer, Irene Sánchez, Catherine Ball, R E Phillips

Bibliographic record

VenueTechnology and Health Care · 2000
Typearticle
Languageen
FieldComputer Science
TopicAI-based Problem Solving and Planning
Canadian institutionsHealth Canada
FundersAgence Universitaire de la Francophonie
KeywordsComputer science

Abstract

fetched live from OpenAlex

the physician will record manually all the planning activities.This is necessary in order to create an affordable process history before converting the system from passive to active in supporting the physicians in their planning activities.All these manual activities will be recorded in both data & knowledge systems and when the clinical staff, responsibles of the project, are sure that the history is affordable, the expert system will be activated as planning support system.Discussion: Management Information System by Object-Process-Activity: The information system must guarantee both the management of the objects and the decisional support to the decision makers inside the object itself; for this second objective we outline the importance of a planning and control model, based on the KKI, able to support: (1) the control, (2) the inefficiency analyses, (3) the identification of the excellences (top quality), (4) the trends analyses and the benchmarking projects, (5) the market's and the territory's feed-backs control, (6) the revenue control, (7) the costs analyses.

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)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.397
Threshold uncertainty score0.860

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0060.003
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.3970.327

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 head0.012
GPT teacher head0.270
Teacher spread0.258 · 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.

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

Citations0
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueTechnology and Health CareSame topicAI-based Problem Solving and PlanningFrench-language works237,207