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Record W4410815205 · doi:10.1001/jamasurg.2025.1386

Familiarity of the Surgeon-Anesthesiologist Dyad and Major Morbidity After High-Risk Elective Surgery

2025· letter· en· W4410815205 on OpenAlexaffabout
Julie Hallet, Angela Jerath, Pablo Pérez d’Empaire, François Martin Carrier, Alexis F. Turgeon, Daniel I. McIsaac, Chris Idestrup, Gianni R. Lorello, Alana M. Flexman, Biniam Kidane, Wing C. Chan, Anna Gombay, Natalie G. Coburn, Antoine Eskander, Rinku Sutradhar

Bibliographic record

VenueJAMA Surgery · 2025
Typeletter
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsPublic Health OntarioUniversity of ManitobaSt. Paul's HospitalProvidence Health CareThe Wilson CentreWomen's College HospitalUniversity of British ColumbiaToronto Western HospitalUniversity Health NetworkHealth Sciences CentreOttawa HospitalInstitute for Clinical Evaluative SciencesUniversity of OttawaCentre Hospitalier de l’Université de MontréalUniversité LavalUniversité de MontréalUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineDyadOdds ratioAnesthesiologyOrthopedic surgeryRetrospective cohort studyLogistic regressionSurgeryGeneral surgeryInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

Importance: The surgeon-anesthesiologist teamwork is a core component of performance in the operating room, which can influence patient outcomes. Objective: To examine the association between surgeon-anesthesiologist dyad familiarity (as dyad volume, the number of procedures done together) with 90-day postoperative major morbidity for high-risk elective surgery. Design, Setting, and Participants: This population-based retrospective cohort study used administrative health care data from Ontario, Canada. Participants included high-risk elective operations (cardiac, low- and high- risk gastrointestinal [GI], genitourinary, gynecology oncology, neurosurgery, orthopedic, spine, vascular, and head and neck) from 2009 through 2019. Data were analyzed from January 2009 to March 2020. Exposure: Dyad familiarity, as the annual volume of procedures done by the surgeon-anesthesiologist dyad in 4 years prior to index surgery. Main Outcomes and Measures: 90-day major morbidity (any Clavien-Dindo grade 3 to 5). The association between exposure and outcome was examined using multivariable logistic regression, stratified by type of procedure. Results: Among 711 006 index procedures, the median dyad volume and rate of 90-day major morbidity varied by type of procedure. There was higher median volume and dyad consistency for cardiac, orthopedic, and lung surgery. For other procedures, the median dyad volume was low (3 or less procedures per dyad per year). An independent association was observed between dyad volume and 90-day major morbidity for high-risk GI surgery (odds ratio [OR], 0.92; 95% CI, 0.88-0.96), low-risk GI surgery (OR, 0.96; 95% CI, 0.95-0.98), gynecology oncology surgery (OR, 0.97; 95% CI, 0.94-0.99), and spine surgery (OR, 0.97; 95% CI, 0.96-0.99), after adjusting for hospital setting, hospital, surgeon and anesthesiologist volume, and patient age, sex, and comorbidity burden. The adjusted associations were not significant for other types of procedures. Conclusions and relevance: In this study, increasing familiarity of the surgeon-anesthesiologist dyad was associated with improved postoperative outcomes for patients undergoing low- and high-risk GI surgery, gynecology oncology surgery, and spine surgery. For each additional time that a unique surgeon-anesthesiologist dyad worked together, the odds of 90-day major morbidity decreased by 4% for low-risk GI surgery, 8% for high-risk GI surgery, 3% for gynecology oncology surgery, and 3% for spine surgery. Additional research is needed to determine the most effective care structures that harness the benefits of surgeon-anesthesiologist familiarity to potentially improve patient outcomes.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.003
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 head0.014
GPT teacher head0.231
Teacher spread0.217 · 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; both teacher heads agree on what is shown here.

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

Citations14
Published2025
Admission routes2
Has abstractyes

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