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Record W2771740152 · doi:10.5430/ijhe.v6n6p106

First Year Specialist Anaesthesia Training in Ireland: A Logbook Analysis

2017· article· en· W2771740152 on OpenAlexvenueno aff
SM O’Shaughnessy, Conor Skerritt, CW Fitzgerald, R.D. Irwin, Fergus Walsh

Bibliographic record

VenueInternational Journal of Higher Education · 2017
Typearticle
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsLogbookAnesthesiaMedicineRegional anaesthesiaGeneral anaesthesiaAirwayFirst lineInternal medicine

Abstract

fetched live from OpenAlex

Objective:Acquisition of a new range of skills occurs during first year anaesthesia training. At present, no defined logbook targets exist for the Irish anaesthesia trainee.The aim of this study was to quantify the number of practical procedures performed and supervision required during first year anaesthesia training.Methods:A retrospective analysis of prospectively maintained logbooks of three first year anaesthesia trainees was performed.Results:In the first three months, mean numbers of cases were 224, enodotrachael tube (ETT) 64, laryngael mask airway (LMA) 55, spinal anaesthetic 12, arterial lines 9.5, central lines 0.5, peripheral nerve blockade (PNB) 2, epidurals 0. There was 91.5% direct supervision and 8.5% indirect supervision.In the final three months, mean numbers of cases were 205.5, ETT 28, LMA 35, spinals 50, arterial line insertions 4.5, central line insertions 1.5, PNB 3.5, epidurals 80. There was 68.5% direct supervision and 31.5% indirect supervision.Conclusions:Defined logbook targets are needed to quantify trainee progress.

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)0.006
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.355
Teacher spread0.327 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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