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Record W4389830103 · doi:10.1002/9781119525127.ch2

The Endoscopy Unit, Staff, and Management

2023· other· en· W4389830103 on OpenAlexaff
Catharine M. Walsh MD MEd PhD FRCPC, Ahmir Ahmad MBBS BSc MRCP, Brian P. Saunders MD FRCP FRCS, Jonathan Cohen MD FASGE FACG, Peter B. Cotton MD FRCP FRCS, Christopher B. Williams BM FRCP FRCS, Stephen Preston

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicEsophageal and GI Pathology
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsEndoscopyMedicineUnit (ring theory)Team leaderMedical emergencyEndoscopeNursingHospital wardSurgeryPsychologyManagement

Abstract

fetched live from OpenAlex

Endoscopy is a team activity, requiring the collaborative talents of many people with different backgrounds and training. Endoscopic procedures can be performed almost anywhere when necessary (e.g. in an intensive care unit), but the vast majority take place in purpose-designed “endoscopy units.” There are two types of endoscopy units: stand-alone, office-based procedure facilities and hospital-based units. There must be designated areas for endoscope and accessory reprocessing, and storage of medications and all equipment, including an emergency resuscitation cart. Specially trained endoscopy assistants have many important functions. Most endoscopy assistants are trained nurses, but technicians and nursing aides also have roles. Complex organizations require efficient management and leadership. This works best as a collaborative exercise between the medical director of endoscopy and the chief nurse or endoscopy nurse manager. Endoscopy units should offer educational resources for all of its users, including patients, staff, and endoscopists.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.951
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0490.022

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 head0.295
Teacher spread0.277 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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