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Record W7024912395

Strategies to Improve Acquistion of Technical Skill in Surgical Residents: From Screening Technical Ability at the Time of Selection to Incorporating Performance Adjuncts during Training

2016· dissertation· en· W7024912395 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTSpace (University of Toronto) · 2016
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicDiverse Legal and Medical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDelphiTest (biology)Selection (genetic algorithm)Task (project management)Learning curveDelphi methodDreyfus model of skill acquisition
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Evidence suggests that not all trainees reach technical competence. Therefore the purposes of the included studies were to improve resident selection by investigating screening tools (visual spatial tests (VSTs) and technical tasks (TTs)) that may predict technical ability of incoming trainees, and to determine whether metal practice is beneficial as a performance enhancement strategy during training. Methods: Screening with VSTs as a predictor of laparoscopic ability was evaluated using the PicSOr, cube comparison (CC) and card rotation (CR) tests and correlated to technical performance on the camera navigation (LCN) and laparoscopic circle cut (LCC) tasks. To screen trainees using TTs, a Delphi of Canadian general surgery (GS) program directors (PD), was performed to gain consensus on the simulated TTs best suited for incoming trainees. K-mean clustering learning curve (LC) analysis was used to determine acquisition of TTs. Next, mental practice was evaluated in a randomized control trial to assess its impact on advanced laparoscopic technical performance. Results: Thirty-seven residents were screened using VSTs. Residents who scored higher on the CC test had more accurate LCN path length (rs(PL) =-0.36, p=0.03) and angle path (rs(AP) =-0.426, p=0.01) scores. Eleven of 14 GS PDs participated in the Delphi, and consensus was reached that both basic laparoscopic and open skills would be appropriate for the assessment of TTs. LC analysis of 65 students revealed that 7-15% of trainees did not reach proficiency in laparoscopic skills. These students demonstrated poor innate ability, and remained disadvantaged with inconsistent performance throughout their LC. During training, mental practice significantly improved technical performance (p =0â 003). Conclusion: LC analysis of simulated technical skills proved more dependable than VSTs to screen for technical ability in novice trainees, while mental practice is an affective adjunct to technical skills performance and would be a beneficial addition to skills training for senior residents.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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