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Retreatments must be included in the evaluation of device performance

2020· article· en· W3085221031 on OpenAlexaff
René Chapot, Pascal J. Mosimann, Tim E. Darsaut, Jean Raymond

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicAortic aneurysm repair treatments
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsWebcastMedicineNeurovascular bundleAneurysmOcclusionRadiologySurgeryWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

We would like to comment on the conclusions of the recent paper reporting the 3 year follow-up results of the Webcast studies1: ‘This analysis confirms the high safety profile of WEB…the great stability of aneurysm occlusion in 83.6% of aneurysms’. It is not easy to criticize a manuscript written by 17 experts, including many prominent leaders of the neurovascular field and several close friends, particularly one where superlatives (‘high safety’ and ‘great stability’) and ‘GCP’ (n=8) are used repeatedly. Yet, as true friends, we must tell the ‘ugly truth’: 10 years after its introduction, the evidence supporting the use of WEB in the treatment of aneurysms (mainly small unruptured aneurysms) is weak; a case series of 100 or so highly selected patients, recruited in …

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)0.045
metaresearch head score (Gemma)0.172
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.172
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0020.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0090.006

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 head0.170
GPT teacher head0.366
Teacher spread0.195 · 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
DomainMethods
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
Published2020
Admission routes1
Has abstractyes

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