Improving splenic conservation rate after trauma by applying a protocol for non-operative management and follow-up: A propensity-score analysis
Bibliographic record
Abstract
Background: There are shared guidelines about Non-Operative Management (NOM) of splenic injuries, but some unanswered questions remain. The aim of the present study is to establish the usefulness of a standardized protocol for management and follow-up of NOM patients with splenic injuries. Methods: Multicenter retrospective observational study including patients with major blunt trauma (ISS >15) with splenic injuries managed between January 1 st 2014 and December 31 st 2016 in two Italian I level Trauma Centers: one with a standardized management and follow-up protocol for NOM (Bufalini Hospital, Cesena, BH), and the other without it (ASST Papa Giovanni XXIII Hospital, Bergamo, PG23H). Comparison between patients’ outcomes were performed and a propensity score model was calculated. Results: 47 patients managed in BH and 49 patients in PG23H were included. In BH, a higher proportion of patients was treated with NOM (72.3 % vs. 53.1 %, p = 0.051). There was no difference in complication rate and mortality in patients treated with NOM in the two hospitals. A borderline significant trend to a higher NOM failure rate in PG23H was found (BH 0.0 % vs. PG23H 11.3 %, p = 0.076). The total splenic conservation rate was significantly higher in BH (BH 72.3 % vs. PG23H 46.9 %, p = 0.011). After the Propensity Score based matching, 72 patients were included and the total splenic conservation rate was significantly higher in BH (BH: 77.8 % vs. PG23H: 50.9 %, p = 0.014). Conclusions: The application of a protocol for in-hospital management and follow-up for NOM of patients with splenic injury could decrease the NOM failure rate and improve splenic conservation rate.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".