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Record W2804782486 · doi:10.1111/tid.12915

Training in transplant infectious diseases: A survey of infectious diseases and transplant infectious diseases fellows in the United States and Canada

2018· article· en· W2804782486 on OpenAlexaffabout
Susanna K. Tan, Nicole Theodoropoulos, Ricardo M. La Hoz, Sherif B. Mossad, Camille N. Kotton, Lara Danziger‐Isakov, Deepali Kumar, Shirish Huprikar

Bibliographic record

VenueTransplant Infectious Disease · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineInfectious disease (medical specialty)Family medicineTransplantationPhoneGerontologyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Infectious diseases (ID) specialists with experience in managing infections in transplant recipients and other immunocompromised hosts are increasingly needed as these fields expand. METHODS: To evaluate experiences and identify trainee-described needs in transplant infectious diseases (TID) training, the American Society of Transplantation, Infectious Diseases Community of Practice (AST IDCOP) surveyed ID fellows across the United States and TID fellows in the United States and Canada and received responses from 203 ID fellows and 13 TID fellows. RESULTS: Among ID fellows, the amount of TID training during ID fellowship was rated between less than ideal and adequate. Reasons cited included limited frequency of didactic activities and limited exposure to transplant patients during training. In particular, ID fellows at low-volume transplantation centers expressed interest in more TID training time, away training opportunities, and specific TID didactics. Educational resources of high interest among trainees were case-based interactive websites, mobile phone applications with TID guidelines, and a centralized collection of relevant articles. Pediatric ID fellows reported lower satisfaction scores with TID training, while TID fellows were overall satisfied or more than satisfied with their training experience. CONCLUSION: Findings from this survey will inform local and national TID educational initiatives.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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

Citations7
Published2018
Admission routes2
Has abstractyes

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