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Record W2488893402 · doi:10.1097/bot.0000000000000403

Making Safe Surgery Affordable

2015· review· en· W2488893402 on OpenAlexaff
Lawrence L. Buchan, Marianne S. Black, Michael A. Cancilla, Elise Huisman, Jeremy J. R. Kooyman, Scott C. Nelson, Nathan N. O’Hara, Peter J. O’Brien, Piotr A. Blachut

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2015
Typereview
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsArbutus Biopharma (Canada)University of British Columbia
Fundersnot available
KeywordsDrillMedicineDrillingMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Many surgeons in low-resource settings do not have access to safe, affordable, or reliable surgical drilling tools. Surgeons often resort to nonsterile hardware drills because they are affordable, robust, and efficient, but they are impossible to sterilize using steam. A promising alternative is to use a Drill Cover system (a sterilizable fabric bag plus surgical chuck adapter) so that a nonsterile hardware drill can be used safely for surgical bone drilling. Our objective was to design a safe, effective, affordable Drill Cover system for scale in low-resource settings. We designed our device based on feedback from users at Mulago Hospital (Kampala, Uganda) and focused on 3 main aspects. First, the design included a sealed barrier between the surgical field and hardware drill that withstands pressurized fluid. Second, the selected hardware drill had a maximum speed of 1050 rpm to match common surgical drills and reduce risk of necrosis. Third, the fabric cover was optimized for ease of assembly while maintaining a sterile technique. Furthermore, with the Drill Cover approach, multiple Drill Covers can be provided with a single battery-powered drill in a "kit," so that the drill can be used in back-to-back surgeries without requiring immediate sterilization. The Drill Cover design presented here provides a proof-of-concept for a product that can be commercialized, produced at scale, and used in low-resource settings globally to improve access to safe 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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.867
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.186
GPT teacher head0.415
Teacher spread0.229 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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