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Record W4322771460 · doi:10.1186/s13741-023-00292-5

Beyond guideline knowledge: a theory-based qualitative study of low-value preoperative testing

2023· article· en· W4322771460 on OpenAlex
Yamile Jasaui, Sameh Mortazhejri, Shawn Dowling, D’Arcy Duquette, Geralyn L’Heureux, Stefanie Linklater, Kelly Mrklas, Gloria Wilkinson, Sanjay Beesoon, Andrea M. Patey, Shannon M. Ruzycki, Jeremy Grimshaw

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePerioperative Medicine · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsUniversity of AlbertaAlberta Health ServicesAlberta HealthOttawa HospitalUniversity of OttawaUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsMedicineTest (biology)Preoperative careCLARITYQualitative researchPreoperative fastingSnowball samplingFamily medicineSurgeryPerioperative

Abstract

fetched live from OpenAlex

BACKGROUND: Choosing Wisely Canada and most major anesthesia and preoperative guidelines recommend against obtaining preoperative tests before low-risk procedures. However, these recommendations alone have not reduced low-value test ordering. In this study, the theoretical domains framework (TDF) was used to understand the drivers of preoperative electrocardiogram (ECG) and chest X-ray (CXR) ordering for patients undergoing low-risk surgery ('low-value preoperative testing') among anesthesiologists, internal medicine specialists, nurses, and surgeons. METHODS: Using snowball sampling, preoperative clinicians working in a single health system in Canada were recruited for semi-structured interviews about low-value preoperative testing. The interview guide was developed using the TDF to identify the factors that influence preoperative ECG and CXR ordering. Interview content was deductively coded using TDF domains and specific beliefs were identified by grouping similar utterances. Domain relevance was established based on belief statement frequency, presence of conflicting beliefs, and perceived influence over preoperative test ordering practices. RESULTS: Sixteen clinicians (7 anesthesiologists, 4 internists, 1 nurse, and 4 surgeons) participated. Eight of the 12 TDF domains were identified as the drivers of preoperative test ordering. While most participants agreed that the guidelines were helpful, they also expressed distrust in the evidence behind them (knowledge). Both a lack of clarity about the responsibilities of the specialties involved in the preoperative process and the ease by which any clinician could order, but not cancel tests, were drivers of low-value preoperative test ordering (social/professional role and identity, social influences, belief about capabilities). Additionally, low-value tests could also be ordered by nurses or the surgeon and may be completed before the anesthesia or internal medicine preoperative assessment appointment (environmental context and resources, beliefs about capabilities). Finally, while participants agreed that they did not intend to routinely order low-value tests and understood that these would not benefit patient outcomes, they also reported ordering tests to prevent surgery cancellations and problems during surgery (motivation and goals, beliefs about consequences, social influences). CONCLUSIONS: We identified key factors that anesthesiologists, internists, nurses, and surgeons believe influence preoperative test ordering for patients undergoing low-risk surgeries. These beliefs highlight the need to shift away from knowledge-based interventions and focus instead on understanding local drivers of behaviour and target change at the individual, team, and institutional levels.

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)0.023
metaresearch head score (Gemma)0.053
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.053
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.629
GPT teacher head0.633
Teacher spread0.004 · 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