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Use of the Ontario Bronchoscopy Assessment Tool to Enhance Post Procedural Feedback for Pulmonary Fellows

2021· article· en· W4234300708 on OpenAlexaboutno aff
Jefferson Chambers, Lynn M Keenan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBronchoscopyFlexible bronchoscopyCompetence (human resources)SedationPulmonary medicineMedical physicsMedical educationSurgeryIntensive care medicinePsychology

Abstract

fetched live from OpenAlex

RATIONALE For many pulmonary fellows, bronchoscopy is the most common procedure performed during fellowship. This vast number of bronchoscopies creates a unique opportunity for frequent assessment of skills, and continual feedback. Despite bronchoscopy being a skill paramount to pulmonary medicine, many fellowship programs lack a process for standardized assessment of skills over time. Additionally, a common concern within graduate medical education, is the desire for more frequent verbal feedback. The Ontario Bronchoscopy Assessment Tool (OBAT) has previously been validated to assess trainee competence with planning and performing bronchoscopy. We became interested in using the OBAT to objectively assess fellow progression in performing bronchoscopy, as well as enhance the satisfaction with feedback surrounding this procedure. METHODS From July 2019 through August 2020 attendings were asked to fill out the OBAT with the fellow after each fellow-performed bronchoscopy. Weekly reminders were sent via text messaging to all attendings within the division. Scores across the 19 categories in the OBAT were compared from the beginning and end of the trail period for two different fellowship classes (first and second years as of July 2019). Following the end of the trial period, surveys were sent to attendings and fellows assessing their satisfaction using the tool. RESULTS There was an average of 23.3 fellow performed bronchoscopies per month with an accompanying monthly OBAT completion rate of 29.6%. The junior fellowship class displayed a larger improvement in average scores. The three most significant improvements were appropriate administration of sedation, inspection in an orderly manner, and assessing for post procedural complications, with a difference of 3, 2.3, and 3 points (5-point scale) respectively. 88% of fellows felt the OBAT was a useful way to receive feedback and the same number stated they preferred the OBAT over traditional, impromptu feedback. 62% of fellows felt feedback happened more frequently because of the OBAT. 66% of attendings felt the tool was helpful way of giving feedback. Most fellows and attendings prefer to continue to use the OBAT in the future. CONCLUSIONS We found the OBAT most beneficial for prompting detailed feedback following a bronchoscopy. Nearly all fellows preferred to receive feedback using the OBAT compared to spontaneous feedback which, at times, was felt to be lacking in substance. We found monitoring individual progress difficult due to low OBAT completion rate. For future utilization, staff education and frequent reinforcement of the goals of the tool may help improve completion rates.

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)0.006
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.300
Teacher spread0.281 · 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 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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