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Record W4389402206 · doi:10.3947/ic.2023.0067

Phage Therapy in Korea: A Prescribers' Survey of Attitudes Amongst Korean Infectious Diseases Specialists Towards Phage Therapy

2023· article· en· W4389402206 on OpenAlexaff
Shinwon Lee, Stephanie Lynch, Ruby C.Y. Lin, Heejoon Myung, Jonathan R. Iredell

Bibliographic record

VenueInfection and Chemotherapy · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsSemtech (Canada)
FundersNational Health and Medical Research Council
KeywordsPhage therapyMedicineInfectious disease (medical specialty)Family medicineClinical trialDiseaseInternal medicineBacteriophageBiology

Abstract

fetched live from OpenAlex

Background: Concerns about the rise in antimicrobial resistance have led to renewed interest in phage therapy worldwide, but perceptions among relevant medical professionals in Korea remain largely unknown. Materials and Methods:We conducted a semi-quantitative online survey to evaluate the Korean infectious disease specialists' perception of phage therapy. Results:We sent out the link to the questionnaire to 380 subjects and received 91 replies, with 90/91 respondents identifying as Korean infectious diseases specialists or trainees.Ten out of 91 (11.0%) respondents scored themselves as well-informed about phage therapy.The majority (93.4%) of respondents would consider using phage therapy if the safety of the phage formulation is guaranteed, and 80% of respondents would consider participating in clinical trials with phage therapy given adequate support.The biggest concern was uncertainty about safety (73.6%) and efficacy (65.9%).Acinetobacter baumannii was ranked as a high priority for phage therapy research, as were bone and joint infections.Conclusion: Korean infectious diseases specialists are receptive to phage therapy, but a better understanding of safety, efficacy and clinical trials are warranted to progress phage therapy within the Korean healthcare system.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.281
Teacher spread0.260 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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