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Record W4206109172 · doi:10.1590/0100-6991e-20213031

Impact of surgical checklist and its completion on complications and mortality in urgent colorectal procedures

2021· article· en· W4206109172 on OpenAlexaffabout
Camila Sarmento Gama, Chantal Backman, Adriana Cristina de Oliveira

Bibliographic record

VenueRevista do Colégio Brasileiro de Cirurgiões · 2021
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsChecklistMedicineLogistic regressionRetrospective cohort studyEpidemiologyMortality rateSurgeryEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: to assess the impact of using a surgical checklist and its completion on complications such as surgical site infection (SSI), reoperation, readmission, and mortality in patients subjected to urgent colorectal procedures, as well as the reasons for non adherence to this instrument in this scenario, in a university hospital in Ottawa, Canada. METHODS: this is a retrospective, epidemiological study. We collected data from an electronic database containing information on patients undergoing urgent colorectal operations, and analyzed the occurrence of SSI, reoperation, readmission, and death in a 30 day period, as well as the completion of the checklist. We conducted a descriptive statistical analysis and logistic regression. RESULTS: we included 5,145 records, of which 5,083 (98.8%) had complete checklists. As for the outcomes evaluated, cases with complete checklists displayed higher SSI rate, 9.1% vs. 6.5% (p=0.466), lower reoperation rate, 5% vs.11.3% (p=0.023), lower readmission rates, 7.2% vs. 11.3% (p=0.209), and lower mortality, 3.0% vs. 6.5% (p=0.108) than cases with incomplete ones. CONCLUSION: there was a high level of checklist completion and a larger number of the outcomes in the reduced percentage of incomplete checklists found, demonstrating the impact of its utilization on the safety of patients undergoing urgent operations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.070
GPT teacher head0.385
Teacher spread0.315 · 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.

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

Citations4
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueRevista do Colégio Brasileiro de CirurgiõesSame topicSepsis Diagnosis and TreatmentFrench-language works237,207