MétaCan
Menu
Back to cohort
Record W2416602472

The clinical interventional radiologist: results of a national survey by the Canadian Interventional Radiology Association.

2006· article· en· W2416602472 on OpenAlexaffabout
Mark O. Baerlocher, Murray Asch, Eran Hayeems, Peter Collingwood

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMedicineInterventional radiologyRemunerationClinical PracticeRadiologyFamily medicineMedical physics
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the level of clinical responsibility interventional radiologists in Canada currently have within their practice and would like to have within their future practices. METHODS: An anonymous online survey was e-mailed to all members of the Canadian Interventional Radiology Association. The survey was open for a period of 2 months. RESULTS: A total of 75 surveys were received, of a possible 247, a response rate of 30.4%. Responses regarding general measures of clinical duties were collected. The current situation in Canada is mixed, in that while most (82%) respondents perform procedures that require an overnight admission, only 11% have a dedicated interventional radiology (IR) ward and 29% have admitting privileges. Most (73%) respondents stated that interventional radiologists in Canada should become more clinical. The most common reason cited for a lack of admitting privileges was a lack of time (44%), followed by a lack of hospital or administrative support (40%), "other" (20%), and inadequate remuneration (14%). CONCLUSIONS: Most respondents believe that interventional radiologists should become more clinically oriented. The most frequently noted obstacles to becoming more clinically oriented are reluctant administration, lack of time, and inadequate remuneration for clinical duties.

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)0.012
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.106
GPT teacher head0.372
Teacher spread0.266 · 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.

Study designObservational
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

Citations19
Published2006
Admission routes2
Has abstractyes

Explore more

Same venuePubMedSame topicRadiology practices and educationFrench-language works237,207