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Record W4322719431 · doi:10.4081/ejtm.2023.11279

Last-minute abstracts of 2023 Padua Days of Muscle and Mobility Medicine (2023 Pdm3) and 2023 Editorial board of EJTM

2023· article· en· W4322719431 on OpenAlexaboutno aff
Sandra Zampieri, Ugo Carraro

Bibliographic record

VenueEuropean Journal of Translational Myology · 2023
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLibrary sciencePolitical scienceFamily medicine

Abstract

fetched live from OpenAlex

The 2023 Padua Days of Muscle and Mobility Medicine (Pdm3) are scheduled from March 29th to April 1st, 2023. The abstracts collected during autumn and early winter of 2022 were e- published in the issue 33 (1) 2023 of the European Journal of Translational Myology (EJTM). Now the last-minute abstracts are reported here (100 Oral presentations are listed in the final Program). All together they confirm the interest of very different international specialists, filling the four days of 2023Pdm3. Indeed, scientists and clinicians from Austria, Bulgaria, Canada, Denmark, France, Georgia, Germany, Iceland, Ireland, Italy, Mongolia, Norway, Russia, Slovakia, Slovenia, Spain, Switzerland, The Netherlands and USA will gather to the Hotel Petrarca of Thermae of Euganean Hills, Padua, Italy. The apparent heterogeneity of the specialists, collectively raccolti under the umbrella of the Mobility Medicine neologism is stressed by the need to extend the Sections of the 2023 Editorial Borad of EJTM also here reported. We hope that Speakers of the 2023 Pdm3 and readers of EJTM will submit "Communications" to the European Journal of Translational Myology by May 20, 2023 and/or to the 2023 Special Issue: "Pdm3" of the Journal Diagnostics, MDPI, Basel, Switzerland with deadline September 30, 2023. See you soon at the Hotel Petrarca of Montegrotto Terme, Padua, Italy. For a promo of the 2023 Pdm3 link to: https://www.youtube.com/watch?v=zC02D4uPWRg.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.032
GPT teacher head0.298
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.

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
Published2023
Admission routes1
Has abstractyes

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