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Record W2888862062 · doi:10.1016/j.anorl.2018.08.004

Young Otolaryngologists of International Federation of Oto-rhino-laryngological Societies (YO-IFOS) committees

2018· editorial· en· W2888862062 on OpenAlexaff
Tareck Ayad, Kate Stephenson, A.L. Smit, Ori Benari, Róbert Késmárszky, Jérôme R. Lechien, Steven E. Sobol, Catherine Meller, Zoukaa Sargi, Rebecca Maunsell, Romolo Daniele De Siati, Huan Jia, Vidya Krishnan, Hannah North, Elie Eter, Osama Metwaly, Shazia Peer, N. Teissier, Leigh J. Sowerby, Paul Hong, Nicolas Fakhry

Bibliographic record

VenueEuropean Annals of Otorhinolaryngology Head and Neck Diseases · 2018
Typeeditorial
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsIzaak Walton Killam Health CentreDalhousie UniversitySt Joseph's Health CareWestern UniversityCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicinePolitical science

Abstract

fetched live from OpenAlex

Young Otolaryngologists (YO) can face many challenges early in their careers. YO in the developing world might lack access to high quality and inexpensive educational material, although open access resources are increasing [1]. YO practicing in geographically isolated countries or modest academic hospitals might struggle to get opportunities for high-quality clinical or research fellowships [2]. Young surgeons who are active in research sometimes lack the opportunity to shine in international meetings because they have limited access to otolaryngology networks, associations and societies.

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)0.008
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.012
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.024
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.001
Science and technology studies0.0030.001
Scholarly communication0.0050.004
Open science0.0020.002
Research integrity0.0110.012
Insufficient payload (model declined to judge)0.0120.009

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 head0.029
GPT teacher head0.320
Teacher spread0.291 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations10
Published2018
Admission routes1
Has abstractno

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