Real-world surgical outcomes of a gelatin-hemostatic matrix in women requiring a hysterectomy: A matched case–control study
Bibliographic record
Abstract
Introduction: The aim of this study was to compare adverse events and surgical outcomes of hysterectomy with or without use of a gelatin-hemostatic matrix (SURGIFLO). Materials and methods: Prospective case–control study (Canadian Task Force classification II2) of total hysterectomy (Piver Type 1) provided by surgeons in Australia between November 2005 and May 2015. Data were collected via SurgicalPerformance, a web-based data project which aims to provide confidential feedback to surgeons about their surgical outcomes. Of 2440 records of women who received a hysterectomy, 1351 were eligible for these analyses; 107 received SURGIFLO hemostatic matrix to prevent postoperative blood loss and 1244 did not. Results: Patients with or without SURGIFLO differed in age, Charlson comorbidity index, and American Society of Anesthesiologists physical status classification system score (ASA), and also differed in clinical outcomes. After matching for patient's age and ASA at surgery, patients with and without SURGIFLO had comparable baseline characteristics. Matched patients with and without SURGIFLO had comparable clinical outcomes including risk of developing vault hematoma, return to the operating room, transfusion of red cells, surgical site infection (pelvis), readmission within 30 days and unplanned ICU admission. Conclusions: In a sample matched by age and ASA, SURGIFLO neither prevented nor caused additional adverse events in women undergoing hysterectomy. Surgeons used SURGIFLO more commonly among women who were older, had more comorbidities and a higher ASA score. This indicates that it may be most useful in complicated surgery or cases.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".