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Record W2428878145 · doi:10.1177/175045890601601004

Hands-Free Technique: Preventing Occupational Exposure during Surgery

2006· review· en· W2428878145 on OpenAlexaff
Bernadette Stringer, Ted Haines

Bibliographic record

VenueJournal of Perioperative Practice · 2006
Typereview
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineOccupational exposureSurgeryHuman immunodeficiency virus (HIV)Emergency medicineFamily medicine

Abstract

fetched live from OpenAlex

Occupational exposure to blood borne pathogens has led to HBV, HCV and HIV infections among surgeons, nurses and other operating room (OR) personnel and, to a lesser degree, patients (Ross et al 2000, The incident investigation teams and others 1997). Of seven OR studies in which an observer or circulating nurse recorded exposures, there was a percuataneous injury in 1.7-15% of all surgeries, and a mucocutaneous contamination in 6.2-50% of all surgeries. (Gerberding et al 1990, Panlilio et al 1991, Popejoy & Fry 1991, Quebbeman et al 1991, Tokars et al 1992, Lynch & White 1993, Stringer, Infante-Rivard & Hanley 2002). Surgeons and residents usually sustained the greatest number of percutaneous and other exposures during surgery.

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)0.003
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.929
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.080
GPT teacher head0.434
Teacher spread0.354 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2006
Admission routes1
Has abstractyes

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