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Record W2125953445 · doi:10.1177/154193120004402610

Disorientation in Minimal Access Surgery: A Case Study

2000· article· en· W2125953445 on OpenAlexaff
Caroline G. L. Cao, Paul Milgram

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2000
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsColonoscopySpatial disorientationTask (project management)SalientMedicineOrientation (vector space)Sigmoid colonComputer sciencePsychologyArtificial intelligenceSurgeryColorectal cancerSimulationEngineering

Abstract

fetched live from OpenAlex

Navigating through minimal access surgical environments such as the human colon can be difficult, even for expert gastroenterologists. “Getting lost” is a common experience for endoscopists, especially within the tortuous sigmoid colon where salient landmarks are not generally available. This paper presents the concept of “getting lost” in endoscoppy as a loss of both global and local spatial orientation. For the endoscopist performing a colonoscopy, the consequence of local disorientation is an inability to continue the procedure, or possibly even injury to the patient, while consequences of global disorientation can be mistaking the location of a lesion, and/or incomplete examination, resulting in misdiagnosis. This study provides important insights into the physical and cognitive constraints of the task of navigating in colonoscopy, contributing to disorientation in the colon. The implications of our findings for the design of navigational aids and training tools are also discussed.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.256
Teacher spread0.234 · 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 designCase report
Domainnot available
GenreEmpirical

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

Citations36
Published2000
Admission routes1
Has abstractyes

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Same venueProceedings of the Human Factors and Ergonomics Society Annual MeetingSame topicSpatial Cognition and NavigationFrench-language works237,207