Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,008 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,061 | 0,044 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».