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Record W2009318875 · doi:10.1001/jama.2015.233

Efforts Seek to Develop Systematic Ways to Objectively Assess Surgeons’ Skills

2015· article· en· W2009318875 on OpenAlexaboutno aff
Tracy Hampton

Bibliographic record

VenueJAMA · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMedical educationMEDLINE

Abstract

fetched live from OpenAlex

Imagine a surgeon is hired based on strong professional recommendations and qualifications, yet subsequent to being hired,many of the surgeon’s patients arereadmittedtotheclinicwithseriouspostoperative complications. It is later discoveredthat thesurgeon lackssufficientmanual dexterity and was therefore making critical technical errorsduringsurgery thatcompromised surgical success and patient health. Suchanoccurrence, thoughsurprising, isnot unheard of. Surgical skills, which include both technical and nontechnical abilities, are often acquired through an apprenticeshipbased system that hasn’t changed considerably over the past century. Although there are systematic processes to assess competencies during training, certification, and recertification of surgeons, they tend to be subjective, inconsistent, and focused primarily on nontechnical skills. This can lead to high variability in surgical performance across surgical specialties, potentially compromising patient care and introducing ethical quandaries. To address the issue, experts have initiatedefforts todevelopand implementobjective, standardized methods for surgical training and assessment of technical surgical skills. Undoubtedly, better skillswill lead to improved patient care and more effective and efficient health care, which will ultimately benefit physicians, patients, and payers. Designing Standards for Residency Training Inadequate assessment of technical surgical skills has been an ongoing topic of debate, and like all aspects of quality of patient care, it has been under increasing scrutiny in recent years (Moorthy K et al. BMJ. 2003;327[7422]:1032-1037; vanHove PD et al. Br J Surg. 2010;97[7]:972-987). “Current assessment of operative skills is based partly on the numbers of procedures that residents participate in without establishing their level of involvement and performance in the case,” said Jonathan Fryer,MD, a professor of surgery and transplantationatNorthwesternUniversityFeinberg School of Medicine, in Chicago. “Faculty assessment of a resident’s overall surgical performance is commonlydone remotelywhenmemorydecay canbeamajor factor.” Rajesh Aggarwal, MD, PhD, who directs the Arnold and Blema Steinberg Medical Simulation Centre at McGill University in Montreal, Canada, and received his surgical training in London, agreed that most countries use references and reports on residents that are subjective, and there’s no guarantee that having to perform a certain number of surgeries will lead to adequate skills. “Quantity doesn’t equal quality,” he said. “But there are efforts to change these things [competency standards], and residency is different today from just 5 years ago.” The focus of assessing residents’ skills in surgery, as well as in other areas of medicine, is evolving into a milestones paradigm, in which trainees are evaluated on whether they have achieved structured learning goals at different points in their education and whether they are progressing toward autonomy with each procedure; however, this often requires more work by busy surgeons who perform resident evaluations. To address this challenge, Fryer and his colleagues have developed the Procedural Autonomy and Supervision System (PASS), which uses what’s called the A variety of research efforts and clinical programs are aimed at developing objective, standardized methods for surgical training and assessment. Medical News&Perspectives .......p782

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)0.232
metaresearch head score (Gemma)0.373
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.232
Threshold uncertainty score0.948

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2320.373
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0220.013
Science and technology studies0.0020.006
Scholarly communication0.0080.013
Open science0.0060.009
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0050.004

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 head0.060
GPT teacher head0.308
Teacher spread0.248 · 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.

Study designNot applicable
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

Citations17
Published2015
Admission routes1
Has abstractyes

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