Reducing the Burden of Surgical Harm
Bibliographic record
Abstract
OBJECTIVE: To perform a systematic review of interventions used to reduce adverse events in surgery. BACKGROUND: Many interventions, which aim to improve patient safety in surgery, have been introduced to hospitals. Little is known about which methods provide a measurable decrease in morbidity and mortality. METHODS: MEDLINE, EMBASE, and Cochrane databases were searched from inception to Week 19, 2012, for systematic reviews, randomized controlled trials (RCTs), and cross-sectional and cohort studies, which reported an intervention aimed toward reducing the incidence of adverse events in surgical patients. The quality of observational studies was measured using the Newcastle-Ottawa Scale. RCTs were assessed using the Cochrane Collaboration's tool for assessing risk of bias. RESULTS: Ninety-one studies met inclusion criteria, 26 relating to structural interventions, 66 described modifying process factors. Only 17 (of 42 medium to high quality studies) reported an intervention that produced a significant decrease in morbidity and mortality. Structural interventions were: improving nurse to patient ratios (P = 0.008) and Intensive Care Unit (ITU) physician involvement in postoperative care (P < 0.05). Subspecialization in surgery reduced technical complications (P < 0.01). Effective process interventions were submission of outcome data to national audit (P < 0.05), use of safety checklists (P < 0.05), and adherence to a care pathway (P < 0.05). Certain safety technology significantly reduced harm (P = 0.02), and team training had a positive effect on patient outcome (P = 0.001). CONCLUSIONS: Only a small cohort of medium- to high-quality interventions effectively reduce surgical harm and are feasible to implement. It is important that future research remains focused on demonstrating a measurable reduction in adverse events from patient safety initiatives.
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 | 0.068 | 0.181 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.012 |
| Bibliometrics | 0.023 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".