Can the Impact of Change of Surgical Teams in Cardiovascular Surgery Be Measured by Operative Mortality or Morbidity? A Propensity Adjusted Cohort Comparison
Bibliographic record
Abstract
OBJECTIVE: Our objective was to examine the impact of team changeover and unfamiliar teams in cardiovascular surgery on traditional clinical outcome measures. BACKGROUND: The importance of teamwork in the operating room is increasingly being appreciated, but the impact on more traditional outcome measures is unclear. METHODS: Elective or urgent cardiovascular procedures were divided into categories: team D (patients who had an operation with a day team); team E (patients who had an operation with an evening team); team C (patients who had an operation which included changeover between a day and evening team). Comparison groups were adjusted using propensity scores. RESULTS: We identified 6698 patients who met inclusion criteria (team D, n =3781; team E, n = 518; team C, n = 2399). After propensity score adjustment,there was an increased skin–skin time of 28 minutes in team C when compared with team D (P < 0.001) and of 21 minutes when compared with team E (P <0.001). There were also more episodes of septicemia among team C patients(OR 1.85, P = 0.013) when compared with team D. Patients operated by a day team had a statistically significantly lower number of ventilated hours and shorter hospital length of stay when compared with team E and team C (P < 0.001 and P < 0.001, respectively). There was no difference between teams in operative death, reoperation for bleeding, blood transfusion, renal failure/dialysis, neurologic events, or deep/superficial wound infections. CONCLUSIONS: The change in operating room personnel from the day team to the evening team added significant length to the total operating department time in cardiovascular surgery; however, its impact on most traditional outcome measures was difficult to demonstrate. More sensitive outcome measures may be required to assess the impact of teamwork interventions.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".