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Record W3107682816 · doi:10.5489/cuaj.7054

We’ll meet again, some sunny day

2020· editorial· en· W3107682816 on OpenAlexaffvenueabout
Michael Leveridge

Bibliographic record

VenueCanadian Urological Association Journal · 2020
Typeeditorial
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsQueen's University
Fundersnot available
KeywordsArt

Abstract

fetched live from OpenAlex

ometime in the late morning of June 25, 2006, I decided I didn't care for medical conferences.I was a resident in the plenary hall at the CUA Annual Meeting in Halifax, stifling a hot burp of donair vapour, muttering dolefully to myself and listing in my chair like a looted schooner.Sometime in the very early morning of June 25, 2006, I had decided that I loved medical conferences.I was in the Liquor Dome in Halifax watching Dr. Paul Johnston tear his shirt off (in fairness, it may have simply immolated in the white-hot masculinity) while moving impossibly up-register at the end of a note-perfect karaoke rendition of "I Believe in a Thing Called Love."The room was erupting, my screaming voice was failing, and to my left two prominent urologists clasped hands overhead in a bros fidelis high-five unmatched since Skid Row's "18 and Life" video.It was very good.A year later, I presented my first academic poster and stickhandled questions from Dr. Michael Jewett; in subsequent conversations, he agreed to take me on as a fellow.This was a bit less electric, but also very good.My point about meetings is not just to celebrate bacchanalia, nor to imply they must be high-stakes and transactional, but to eulogize what we lost in 2020 and to hope for the return of these edifying and educational weekends.It seems obvious to click our heels and pine for before times, but it is more complicated under examination.Dr. John Ioannidis succinctly, if dryly, suggested in 2012 that medical meetings served to "disseminate and advance research, train, educate, and set evidence-based policy," but claimed that almost none of these goals required the massive migration of thousands to the "artificial cities" of conference centers. 1 Similar commentaries have reasonably bemoaned the enormous carbon footprint involved in conference travel, inaccessibility to many interested would-be attendees, and even perversions of quality science through dilution and of prestige through manufactured celebrity.[1][2][3] "Networking" is the ever-ready answer to the doubters -the meeting of like minds to open new doors and collaborations, an annuity that will bear fruit at next year's meeting and the one after that in a virtuous cycle.Good science resulting in good medicine and good education is of extremely high value.Perhaps Twitter or other virtual technologies can serve as backchannels for conference discussion and foster new collaboration, but is there any replacement for unhurried and spontaneous banter in real life, the shared experience of a new city and new experiences lubricating new friendships more authentically than in silico connection?Keep them both for sure, but I wouldn't declare a winner just yet.It also just feels so casual to draw such crisp lines when thinking about meetings.A quick brainstorm elicits any number of knock-on effects to a meeting's structure (I'll leave you to think on which are wins and which are losses).Less emissions from travel.Less grant money funnelled to airlines and hotels.Cities and communities where conference activity is a linchpin of employment and the economy.Fewer spoils for the conspicuous baggage tag set may disincentivize participation of thought leaders.Flaky or absent Wi-Fi determining go/no-go status for participation.Access by attendees who are remote/on call/caregivers.Lost links between meeting sponsors and clinicians (that is, the exhibit hall).Universal video capture for post-session broadcast to increase reach.Differences in the type and number of abstract submissions.You may have others, but surely it is not a winner-takes-all equation.From an educational and content standpoint, the CUA and other organizations deserve immense credit for navigating the stress and logistics of cancellations and for the huge amount of work in composing and delivering virtual meetings this year, and have learned many lessons about what works and what suffers online.I submit that among the most important may not be first to mind -the captivity of the audience.In-person meetings carry a number of sunk costs; the participant has paid in time andWe'll meet again, some sunny day EDITORIAL

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)0.003
metaresearch head score (Gemma)0.019
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 score0.061
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0030.001
Scholarly communication0.0060.004
Open science0.0020.001
Research integrity0.0080.014
Insufficient payload (model declined to judge)0.0610.044

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 head0.018
GPT teacher head0.291
Teacher spread0.273 · 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

Citations0
Published2020
Admission routes3
Has abstractyes

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