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Record W3007674412 · doi:10.1055/s-0040-1702158

International Microsurgery Club: An Effective Online Collaboration System

2020· article· en· W3007674412 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Reconstructive Microsurgery · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineClubDemographicsMicrosurgeryResource (disambiguation)Retrospective cohort studySurgeryDemographyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: This study aimed to determine if International Microsurgery Club (IMC) is an effective online resource for microsurgeons worldwide, in providing an avenue for timely group discussions and advice regarding complicated cases, and an avenue for collaboration and information sharing. METHODS: All posts on the IMC Facebook group from member 1 to 8,000 were analyzed according to inclusion criteria and categorized into three categories-case discussion, question, and information sharing. Posts were retrospectively analyzed for number of responses, time of responses, number of "likes," number of treatment options, time of day, and demographics of authors and responders. RESULTS: A retrospective analysis of 531 cases showed an average response rate of 75.7% within 1 hour and as membership grew. The response rate stabilized averaging between 72.5 and 78% across all times of the day. An average of 11.8 microsurgeons was involved per case discussion, and 5.7 treatment options were provided per case. CONCLUSION: IMC is shown to be an effective resource to allow microsurgeons to access timely advice from other microsurgeons without time and distance limitation, and to have interactive group discussions on complicated cases.

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.

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)0.005
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 score0.356
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.054
GPT teacher head0.369
Teacher spread0.315 · 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