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Record W4319656805 · doi:10.3791/6530

Erratum: Magnetic Resonance-Guided High Intensity Focused Ultrasound Generated Hyperthermia: A Feasible Treatment Method in a Murine Rhabdomyosarcoma Model

2023· erratum· en· W4319656805 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Visualized Experiments · 2023
Typeerratum
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineUniversity hospitalFamily medicine

Abstract

fetched live from OpenAlex

An erratum was issued for: Magnetic Resonance-Guided High Intensity Focused Ultrasound Generated Hyperthermia: A Feasible Treatment Method in a Murine Rhabdomyosarcoma Model . The Authors section was updated from: Claire Wunker1,2 Karolina Piorkowska3 Ben Keunen3 Yael Babichev2 Suzanne M. Wong3,4 Maximilian Regenold5 Michael Dunne5 Julia Nomikos1,2 Maryam Siddiqui6 Samuel Pichardo6 Warren Foltz7 Adam C. Waspe3,8 Justin T. Gerstle3,9 Rebecca A. Gladdy1,2,10 1 Institute of Medical Science, University of Toronto 2 2Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital 3 The Wilfred and Joyce Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The Hospital for Sick Children 4 Institute of Biomedical Engineering, University of Toronto 5 Leslie Dan Faculty of Pharmacy, University of Toronto 6 Departments of Radiology and Clinical Neurosciences, University of Calgary 7 Department of Radiation Oncology, University of Toronto 8 Department of Medical Imaging, University of Toronto 9 Department of Pediatric Surgery, University of Toronto 10 Department of Surgery, University of Toronto to: Claire Wunker1,2 Karolina Piorkowska3 Ben Keunen3 Yael Babichev2 Suzanne M. Wong3,4 Maximilian Regenold5 Michael Dunne5 Julia Nomikos1,2 Maryam Siddiqui6 Samuel Pichardo6 Warren Foltz7 Adam C. Waspe3,8 Justin T. Gerstle3,9 James M. Drake1,3,4,10 Rebecca A. Gladdy1,2,10 1 Institute of Medical Science, University of Toronto 2 Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital 3 The Wilfred and Joyce Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The Hospital for Sick Children 4 Institute of Biomedical Engineering, University of Toronto 5 Leslie Dan Faculty of Pharmacy, University of Toronto 6 Departments of Radiology and Clinical Neurosciences, University of Calgary 7 Department of Radiation Oncology, University of Toronto 8 Department of Medical Imaging, University of Toronto 9 Department of Pediatric Surgery, University of Toronto 10 Department of Surgery, University of Toronto.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0160.011

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 head0.050
GPT teacher head0.371
Teacher spread0.321 · 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 designBench or experimental
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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