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Record W4403703959 · doi:10.1097/gox.0000000000006244

Cost-reduction Analysis of Percutaneous Pinning of Hand Fractures in an Outpatient Clinic

2024· article· en· W4403703959 on OpenAlexaffabout
Annabelle Chartier, Ashley Arpin, Valérie Gervais

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2024
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsUniversité de SherbrookeUniversité de Montréal
Fundersnot available
KeywordsMedicinePercutaneousPercutaneous pinningAmbulatoryOutpatient clinicOutpatient surgeryAmbulatory careSurgeryEmergency medicineHealth careInternal medicineInternal fixation

Abstract

fetched live from OpenAlex

Background: The University of Sherbrooke's Hospital Center operating room has been affected by the COVID-19 pandemic, prompting surgeons to seek alternative ways to treat acute injuries requiring surgery. In the spring of 2020, we began performing percutaneous pinning of hand fractures in our outpatient clinic. We aimed to estimate the savings in 2021 by transferring these procedures from the operating room to the outpatient clinic. Methods: We identified all patients with hand injuries who received percutaneous pinning in 2021 using billing codes. Only patients treated in the outpatient clinic were included. We estimated the cost of hand fracture fixation in the operating room by considering the anesthesiologist's fee, the hospital's hourly rate for a 1-hour surgery (including a respiratory therapist, 2 nurses, and equipment) and salary bonuses for unfavorable hours, subtracting the cost difference of outpatient equipment. Results: We identified 114 patients treated with percutaneous pinning, of whom 93 were included in our study. Our calculations showed a total cost reduction of CAD $55,789 in 2021. Conclusions: Percutaneous pinning of hand fractures in an outpatient setting resulted in a yearly cost reduction of more than CAD $55,000. Investing in ambulatory care for hand fracture management benefits both patients and institutions.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.356
Teacher spread0.316 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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