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Record W4313482897 · doi:10.2106/jbjs.22.01111

This Won’t Hurt (No, Really)

2023· letter· en· W4313482897 on OpenAlexaff
James P. Waddell

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2023
Typeletter
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineAbrasion (mechanical)Circular sawPopulationEmergency departmentPhysical therapySurgeryEngineeringBlade (archaeology)NursingMechanical engineering

Abstract

fetched live from OpenAlex

Commentary The high-pitched sound of the cast saw is the background music to the fracture clinic experience for patient and practitioner alike. The need for continual reassurance from the person handling the saw to the person having the cast removed is also a constant feature of the plaster room. I am confident in stating that every orthopaedic surgeon is aware of the potential for injury occurring during cast removal, and I am certain that most if not all of us can still recall instances in which such injury has occurred in our patients. The use of electric cast saws for the removal of plaster or fiberglass circular casts is almost universal both in outpatient clinics and, less commonly, in the emergency department. Injury occurs as a result of unintended contact between the vibrating blade and unprotected skin1. The injury may be an abrasion, a laceration, or a thermal injury, depending on a number of factors: blade temperature, duration of contact, longitudinal movement of the blade, and pressure applied to the blade as it penetrates the cast material. These injuries appear to be more common in the pediatric population, as reported in the published literature, perhaps because the use of circular casts for fracture treatment is more common in this patient group. Different strategies have been promoted to minimize the risk of cast saw injury (e.g., education2,3, safety strips4, and training on models5) and, while these strategies have been of some value in reducing the risk of injury, the benefit may be transient as training must be repeated on a regular basis and must be provided to a wide variety of practitioners, including new residents, physician assistants, emergency room doctors, and cast technicians. The article by Sevencan outlines a novel technique to help minimize the risk of this iatrogenic injury. The author has designed a simple guide to assist in safe cast removal by using an external guide attached to a metal safety strip that fits under the cast. The external guide ensures that the blade of the cast saw does not deviate from the line of the skin protector; furthermore, the skin protector is metal and cannot be breached by the saw blade. In addition to comparing the incidence of skin injury between matched groups (one group with “routine” cast removal and the second with the protective device), the author performed a survey regarding the anxiety level associated with both techniques. In a novel twist, he measured the anxiety level in patients and in those removing the casts. Not surprisingly, the anxiety level was significantly reduced in the patients for whom the protective device was used; somewhat surprising to me was the degree of anxiety reduction in the practitioners removing the casts. The results of the study demonstrate a significant decrease in skin injury in association with the use of the device as well as improved patient satisfaction and provider acceptance of the benefits of using such a device. As described in the article, the device is simple to manufacture, easy to use, and beneficial to patients and practitioners. What’s not to like? As I asked in my original review of the manuscript, “Why didn’t I think of this?” I would not be surprised if this device or a commercial version incorporating the concept will soon be a standard item in plaster rooms, emergency departments, and operating rooms everywhere.

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)0.003
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.025
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.006
Scholarly communication0.0040.006
Open science0.0040.002
Research integrity0.0230.043
Insufficient payload (model declined to judge)0.0250.016

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 head0.119
GPT teacher head0.390
Teacher spread0.270 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2023
Admission routes1
Has abstractyes

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