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Record W2974303562 · doi:10.1177/2054358119875989

A Survey of Training for Temporary Hemodialysis Catheter Insertion During Nephrology Fellowship in Canada: An Update

2019· article· en· W2974303562 on OpenAlexaffabout
Richard Hae, Daniel Samaha, Pierre-Antoine Brown, Rory McQuillan, Swapnil Hiremath, Edward G. Clark

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2019
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsUniversity of TorontoUniversity Health NetworkOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineNephrologyHemodialysisInternal medicineHemodialysis CatheterIntensive care medicineEmergency medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Controversy exists as to whether the insertion of temporary hemodialysis catheters (THDCs) should remain a mandatory requirement of nephrology fellowship training in Canada. A survey conducted by our group in 2012 showed that many nephrology trainees reported inadequate training to achieve procedural competence. OBJECTIVE: To determine the current practices and training of the insertion of THDCs in nephrology fellowship programs in Canada and how this has evolved since 2012. DESIGN: A survey study was designed comprising the following sections: demographics, details regarding the number and types of THDCs inserted within the past 6 months of fellowship training, adherence to sterile techniques, the use of ultrasound guidance during THDC insertion, training for THDC insertion received before and during nephrology fellowship, and self-perceived adequacy of training and competence in THDC insertion. SETTING: The survey was distributed by e-mail in May 2018 either directly or through Canadian nephrology training programs. PARTICIPANTS: Current trainees of Canadian adult nephrology training programs. MEASUREMENTS: Descriptive statistics were used to analyze the summarized data. The means and interquartile ranges (IQRs) were used to summarize the number of THDC insertions performed, and the categorical data, including data on training and self-perceived competency, were reported using frequencies and percentages. A chi-squared test was used to evaluate the relationship between those who received simulation-based training and self-perceived confidence in either internal jugular or femoral catheter insertion. METHODS: An online survey, available in both English and French, was distributed to all adult nephrology trainees in Canada in May 2018 either directly or through their respective programs. RESULTS: Completed surveys were received from 46 of 136 nephrology trainees across Canada (34%). Of those who responded, the median (IQR) number of combined femoral and/or internal jugular THDCs inserted in the past 6 months of fellowship training was 3 (1-6). Eight respondents (17%) indicated that they had not inserted a THDC in the past 6 months. However, only 7 of 42 respondents (17%) indicated that they did not feel competent or adequately trained to perform either femoral or internal jugular THDC insertion. LIMITATIONS: Limitations of the study include participation of trainees at different stages of their training. Many trainees indicated that it was not a requirement to keep a formal log of their procedures performed and likely had recall bias when reporting their procedure details. CONCLUSIONS: Nephrology fellows in Canada are performing fewer THDC insertions compared to 2012 but report higher levels of self-perceived competence and better training. This may be as a result of significantly more simulation-based training. Our data suggest that training to procedural mastery using simulation-based techniques may be a path to ensuring adequate training for THDC insertion despite fewer procedures being performed during training.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.053
GPT teacher head0.315
Teacher spread0.262 · 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 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

Citations0
Published2019
Admission routes2
Has abstractyes

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