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Record W4415300115 · doi:10.1186/s13643-025-02937-6

Conceptualizations of anaesthetists’ clinical reasoning expertise: protocol for a systematic review and qualitative thematic synthesis

2025· review· en· W4415300115 on OpenAlexaff
Jean‐Noël Evain, Mathilde Giffard, Julien Picard, Issam Tanoubi, Sébastien Pili Floury, Guillaume Besch, David Ferreira

Bibliographic record

VenueSystematic Reviews · 2025
Typereview
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsProtocol (science)Thematic analysisQualitative researchThematic mapMEDLINEData collection

Abstract

fetched live from OpenAlex

BACKGROUND: Expertise involves a high level of knowledge or skill in a specific area. Medical expertise encompasses knowledge, technical skills, and socio-cognitive skills like clinical reasoning, essential for accurate diagnosis and treatment. Although traditionally seen as technicians, anaesthesiologists are vital cognitive experts in the operating room, where situational awareness and decision-making are crucial in high-risk, fast-paced situations prone to cognitive bias. Properly defining the cognitive aspects of anaesthetic expertise is challenging, hindering research and educational consistency. This study aims to identify, appraise, and synthesize how expertise within clinical reasoning among anaesthetists is conceptualized in the literature. METHODS: We will search Medline, Embase, and Cochrane databases for peer-reviewed papers up to July 1, 2024, focusing on anaesthesiology, expertise, and clinical reasoning. Our searches will include related terms and citations. According to the PRISMA flow chart, two reviewers will independently screen titles, abstracts, and full texts against inclusion criteria, excluding papers focusing solely on technical expertise. A third reviewer will resolve any disagreements. Information on references, article type, research area, anaesthetic field, and conceptualizations of clinical reasoning expertise will be extracted using a standardized form. To achieve an operational synthesis, a two-stage qualitative analysis will be conducted. The first stage involves a comprehensive semantic analysis to identify patterns and thematic clusters. The second stage follows the Thomas and Harden approach for formal thematic synthesis, using codes to develop categories that lead to descriptive themes. The resulting multi-layered tree structure will ultimately enable generating analytical themes. DISCUSSION: A clear concept synthesis of clinical reasoning expertise among anaesthetists could enhance research, education, and guidelines, thereby improving patient safety. The proposed systematic review and qualitative thematic synthesis aims to clarify this complex concept by analysing data from diverse scientific literature. A broad research strategy will be employed, followed by rigorous qualitative analysis, including semantic analysis and thematic synthesis, to capture the multifaceted nature of clinical reasoning. This study will be the first to propose a global approach, facilitating improved pedagogical interventions and integrating insights into AI models for enhanced training and clinical decision-making. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration number CRD42024510184.

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 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)0.020
metaresearch head score (Gemma)0.661
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.641
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.661
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0320.004
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.235
GPT teacher head0.572
Teacher spread0.337 · 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 teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes1
Has abstractyes

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