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Record W2033203635 · doi:10.3917/rindu.144.0089

La robotique d'assistance à la chirurgie : pourquoi, et comment ?

2014· article· fr· W2033203635 on OpenAlexaff
Clément Vidal

Bibliographic record

VenueAnnales des Mines - Réalités industrielles · 2014
Typearticle
Languagefr
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsFrancophone University Association
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

À l’ère de la robotisation des tâches, la médecine présente une singularité : les variations anatomiques et de pathologies entre les patients font qu’elle ne peut être réduite à des gestes parfaitement reproductibles. Les actes chirurgicaux sont nécessairement individualisés, ils sont spécifiques à chaque patient. La robotique médicochirurgicale est donc confrontée à cette difficulté qui fait que celle-ci se distingue fortement du reste de la robotique. Si les premières approches en robotique d’assistance chirurgicale se sont fortement inspirées de la robotique industrielle, des solutions de plus en plus dédiées sont soit déjà à l’œuvre dans les blocs opératoires soit à l’étude dans les laboratoires de recherche.

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)0.008
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.010
Scholarly communication0.0070.008
Open science0.0020.002
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0080.006

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 head0.084
GPT teacher head0.341
Teacher spread0.257 · 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 designTheoretical or conceptual
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
Published2014
Admission routes1
Has abstractyes

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