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Record W202693461

Surgical procedure logging with use of a hand-held computer.

2002· article· en· W202693461 on OpenAlexaffabout
Sandra E. Fischer, Stephen E. Lapinsky, Jason Weshler, F.T. Howard, Lorne Rotstein, Zane Cohen, Thomas E. Stewart

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsMedicineUploadLoggingThe InternetData loggerDatabaseMedical emergencyMedical physicsWorld Wide WebComputer scienceOperating system
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the feasibility of incorporating hand-held computing technology in a surgical residency program, by means of hand-held devices for surgical procedure logging linked through the Internet to a central database. SETTING: Division of General Surgery, University of Toronto. DESIGN: A survey of general surgery residents. METHODS: The 69 residents in the general surgery training program received hand-held computers with preinstalled medical programs and a program designed for surgical procedure logging. Procedural data were uploaded via the Internet to a central database. Survey data were collected regarding previous computer use as well as previous procedure logging methods. MAIN OUTCOME MEASURE: Utilization of the procedure logging system. RESULTS: After a 5-month pilot period, 38% of surgical residents were using the procedure-logging program successfully and on a regular basis. Program use was higher among more junior trainees. Analysis of the database provided valuable information on individual trainees, hospital programs and supervising surgeons, data that would assist in program development. CONCLUSIONS: Hand-held devices can be implemented in a large division of general surgery to provide a reference database and a procedure-logging platform. However, user acceptance is not uniform and continued training and support are necessary to increase acceptance. The procedure database provides important information for optimizing trainees' educational experience.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.246
Teacher spread0.169 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations23
Published2002
Admission routes2
Has abstractyes

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