Financial Barriers to Surgical Conferences: A Cross‐Sectional Analysis of Registration Fees
Bibliographic record
Abstract
BACKGROUND: Scientific meetings provide much educational value to participants of all career stages. There is a paucity of literature surrounding the costs of attending scientific meetings and how this may affect participation, especially among trainees. The objective of this study is to assess the accessibility of surgical conferences for attendees by analyzing costs related to surgical society membership and conference registration. METHODS: Societal membership and conference registration fee data were collected according to career stage (i.e., student, resident, fellow, and staff) for the fourteen surgical specialties recognized by the American College of Surgeons (ACS). Fees for participants from low- and middle-income countries (LMICs) and for virtual-only attendance options were also collected when available. RESULTS: Overall, we included data from 46 surgical societies (32 North American, 14 European or global). The median conference fees for students in the member and non-member categories were 191.55 USD (IQR 42.22-320.99) and 452.40 USD (IQR 294.06-555.00), respectively, representing a 136.2% price increase if not a member. Median conference fees for residents, fellows, and staff in the member category were 65.5%, 66.9%, and 230.9% greater than that for students, respectively. Median prices for residents, fellows, and staff in the non-member category were 49.9%, 54.9%, and 49.9% greater than that for member trainees of the same category, respectively. CONCLUSIONS: Our results highlight the substantial costs associated with attending surgical conferences, especially for trainees, representing a significant barrier to already financially burdened trainees, especially those from LMICs, smaller institutions, or less well-off backgrounds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".