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Record W2472362342

Social Media and Orthopaedics: Opportunities and Challenges.

2016· article· en· W2472362342 on OpenAlexaff
Tanishq Suryavanshi, Geier Cd, Leland Jm, Lori L. Silverman, Naven Duggal

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsSocial mediaSocial connectednessHealth careMedicinePublic relationsOrthopedic surgeryInternet privacyMedical educationPsychologyWorld Wide WebSurgeryComputer sciencePolitical scienceSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

Social media presents unique opportunities and challenges for practicing orthopaedic surgeons. Social media, such as blogging, Facebook, and Twitter, provides orthopaedic surgeons with a new and innovative way to communicate with patients and colleagues. Social media may be a way for orthopaedic surgeons to enhance communication with patients and healthcare populations; however, orthopaedic surgeons must recognize the limitations of social media and the pitfalls of increased connectedness in patient care.

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)0.007
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0030.006
Scholarly communication0.0100.016
Open science0.0010.005
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0130.003

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 head0.402
GPT teacher head0.353
Teacher spread0.049 · 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
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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