Operative length independently affected by surgical team size: data from 2 Canadian hospitals
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.
Bibliographic record
Abstract
BACKGROUND: Knowledge of the composition of a surgical team is the premise for studying efficiency inside the operating room. METHODS: To investigate the team composition in general surgery procedures, we retrospectively reviewed procedures performed by an expert general surgeon in 2007-08 at 2 tertiary hospitals. For each patient, demographic characteristics, procedure type, team members and procedure length were extracted from intraoperative nursing records. We assessed procedure complexity using a calculated index. Multiple logistic regressions were performed to assess the association between procedure length and team size after adjusting for procedure complexity and patient condition. RESULTS: For the 587 procedures reviewed, the mean procedure length was 88 (standard deviation [SD] 51) minutes. On average, 8 team members (range 4-14), including surgeons, anesthesiologists, nurses and other specialists, were involved in each procedure. Only 47 (8%) procedures were performed by 1 surgeon. Most were performed by 2 (295 [50%]) or 3 surgeons (214 [36%]). Half the team members were nurses (mean 4, range 1-7). Both the complexity of the operation and the team size affected the procedure length significantly. When procedure complexity and patient condition were constant, adding 1 team member predicted a 7-minute increase in procedure length. CONCLUSION: This study demonstrates that a frequent change of core team members has a negative impact on surgical performance. Management strategies need to improve to optimize team efficiency in the operating room.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it