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Record W2934507994 · doi:10.18332/tpc/105180

Preparing the new generation of Romanian experts in tobacco control: PhD studies in the field

2019· article· en· W2934507994 on OpenAlexfundno aff
Ioana Buculei Porosnicu, Cristina Vicol, Cristinel Ștefănescu, Antigona Trofor

Bibliographic record

VenueTobacco Prevention & Cessation · 2019
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsnot available
FundersThird Health ProgrammeUniversity of WaterlooCanadian Institutes of Health ResearchEuropean Commission
KeywordsRomanianTobacco controlField (mathematics)Clinical PracticePolitical scienceEnvironmental healthMedicineHumanitiesPsychologyArtFamily medicinePhilosophyPathologyMathematics

Abstract

fetched live from OpenAlex

Scientific progresses and new regulatory global context about tobacco use and cessation have created attractive opportunities for young researchers seeking for a PhD in the field. As such, three most relevant ongoing PhD research topics at the University of Medicine and Pharmacy “Grigore T. Popa” Iasi were selected for presentation: 1. “Air pollution and tobacco smoke interactions in COPD” (to investigate cumulated effect of tobacco smoking and air pollution on clinical course and treatment benefits in COPD, in the actual upsetting increased local environmental pollution frame). 2. “Smoking and oxidative stress in mixed anxious and depressive disorder”( to reveal interactions between oxidative stress and tobacco smoking in mixed anxious an depressive disorders, to ascertain clinical practice benefits of tobacco exposure biomarkers in use and propose new such markers 1 to improve disease management). 3. “Personalized tobacco treatment" algorithms for smokers at high respiratory risk” (to develop and implement clinical practice personalized algorithms to treat tobacco dependence in smokers at high risk for respiratory disease and to test their feasibility in a real life respiratory disease clinical setting).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.057
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.063
GPT teacher head0.343
Teacher spread0.280 · 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 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 routes1
Has abstractyes

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