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Record W3121706983 · doi:10.1136/bmjresp-2020-000834

Protocol for the Stather Canadian Outcomes Registry for Chest ProcedurEs (SCOPE)

2021· article· en· W3121706983 on OpenAlexafffundabout
Alain Tremblay, Alex Chee, Inderdeep Dhaliwal, Elaine Dumoulin, Ashley Gillson, Paul MacEachern, Michael Mitchell, Colin Schieman, Daniel Stollery, Li Pen, Marc Fortin, Chung Chun Tyan, Erik Vakil, Christopher A. Hergott

Bibliographic record

VenueBMJ Open Respiratory Research · 2021
Typearticle
Languageen
FieldMedicine
TopicPleural and Pulmonary Diseases
Canadian institutionsUniversité LavalWestern UniversityUniversity of SaskatchewanUniversity of AlbertaUniversity of Calgary
FundersUniversity of Calgary
KeywordsMedicineScope (computer science)Protocol (science)Patient registryResearch ethicsRelevance (law)Family medicineMedical educationMedical emergencyAlternative medicinePathologyComputer sciencePediatrics

Abstract

fetched live from OpenAlex

INTRODUCTION: The Stather Canadian Outcomes registry for chest ProcedurEs (SCOPE registry) is a Canadian multicentre registry of chest procedures. METHODS AND ANALYSIS: The SCOPE registry is designed as a multicentre prospective database of specific bronchoscopic or other pulmonary procedures. Each procedure of interest will be associated with a registry module, and data capture designed to evaluate effectiveness of procedures on relevant patient outcomes. Participating physicians will be asked to enter data for all procedures performed in a given module. The anonymised dataset will be housed in a web-based electronic secure database. Specific modules included will be based on participating physician suggestions, capacity and consensus of the steering committee and relevance of hypotheses/research potential. ETHICS AND DISSEMINATION: The central registry is under approval from the Conjoint Health Research Ethics Board at the University of Calgary. We aim for registry data to lead to publication of manuscripts in international medical journals as the primary mode of dissemination. Data may also be used by local investigators for personal and/or institutional quality control purposes as well as to inform health policies. Data requests from non-participating investigators for use under ethics approved research protocols can be considered.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.052
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.448
GPT teacher head0.562
Teacher spread0.114 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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

Citations8
Published2021
Admission routes3
Has abstractyes

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