A Survey of the Practice of After-Hours and Emergency Endoscopy in Canada
Bibliographic record
Abstract
OBJECTIVE: To determine staffing and practice patterns for after-hours endoscopy service in Canada. METHODS: A link to a web-based survey was sent by e-mail to all clinical members of the Canadian Association of Gastroenterology in February 2011. A priori, it was planned to compare variations in practice among gastroenterologists (GIs) performing endoscopy in different regions of Canada, between pediatric and adult GIs, and between university and community hospitals. RESULTS: Of 422 potential respondents, 168 (40%) responded. Of the 139 adult GIs, 61% performed after-hours endoscopy in the endoscopy suite where daytime procedures were performed, 62% had a trained endoscopy nurse available for all procedures, 38% had access to propofol sedation, 12% reprocessed the endoscopes themselves or with the help of a resident, 4% had out-of-hospital patients come directly to their endoscopy suite and 53% were highly satisfied. The adult endoscopists practising at community hospitals were more likely to have an anesthetist attend the procedure. Regional differences were noted, with more involvement of anesthetists (13%) and availability of propofol (50%) in Ontario, more frequent reprocessing of endoscopes in the central reprocessing units in British Columbia (78%) and almost universal availability of a trained endoscopy nurse (96%) with concomitant higher endoscopist satisfaction (84% highly satisfied) in Alberta. CONCLUSIONS: More than one-third of surveyed endoscopists across the country do not have a trained endoscopy nurse to assist in after-hours endoscopy - the time period when urgent patients often present and typically require therapeutic endoscopic interventions. There are significant regional differences in the practice of after-hours endoscopy in Canada.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".