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Record W8008890

Resident training and the dictated operative report: a national perspective.

2010· article· en· W8008890 on OpenAlexaffabout
Lawrence M. Gillman, Ashley Vergis, Krista Hardy, Jason Park, Mark Taylor

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDictationMedicineMedical educationActive listeningCurriculumVignetteQuality (philosophy)Reading (process)MEDLINE
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Using a nationwide survey, we aimed to determine the current status of operative dictation training in Canada. METHODS: Residents and program directors in general surgery programs in Canada participated in this survey. RESULTS: In all, 274 residents and 11 program directors responded to the survey (70% and 79% response rates, respectively). Among residents, 73% reported that their dictations were in need of improvement, and 56% reported never receiving feedback about their dictations. Most residents (80%) stated that they learned to dictate by reading old operative dictations, 75% reported that their program did not use any formal methods to help improve dictations, and 70% requested further training in dictation. In all, 91% of program directors felt that residency programs should include formal training in dictation but half could not identify any formal methods currently used in their programs. CONCLUSION: There appears to be a marked deficiency in resident training in operative dictation nationwide.

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)0.001
metaresearch head score (Gemma)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.848

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.308
Teacher spread0.277 · 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".

Quick stats

Citations20
Published2010
Admission routes2
Has abstractyes

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