PDCA cycle management combined with detailed management of postoperative deep vein thrombosis in patients undergoing hip replacement surgery.
Bibliographic record
Abstract
OBJECTIVE: This study aimed to analyze and explore the effect of Plan-Do-Check-Act (PDCA) cycle management combined with detailed management on postoperative deep venous thrombosis in patients undergoing hip replacement surgery. PATIENTS AND METHODS: Patients who underwent hip replacement surgery in our hospital between November 2021 and April 2023 were recruited for the study. After screening, patients who met all the inclusion criteria were assessed for eligibility. Finally, 80 adults were enrolled. All patients were assigned into observation and control groups (1:1) according to the sequence of admission, i.e., patients admitted between November 2021 and August 2022 were the control group, and patients admitted between September 2022 and April 2023 were the observation group. RESULTS: The intraoperative blood loss and hospital stay in the observation group were significantly less than those in the control group (p<0.05). After the intervention, the levels of plasma prothrombin time (PT), thrombin time (TT), and thromboplastin time (APTT) in the observation group were higher than those in the control group, and the DD level was lower than that in the control group (p<0.05). There was one patient in the observation group who developed deep venous thrombosis after the operation, and the incidence was 2.50%. The rate was significantly lower than that of the control group (p<0.05). The hip joint function score of the observation group was higher than that of the control group, and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scale score was lower than that of the control group (p<0.05). The incidence of adverse reactions in the observation group was significantly lower than that in the control group (p<0.05). CONCLUSIONS: PDCA cycle management plus detailed management in patients with hip replacement surgery yields a favorable clinical outcome, which can effectively prevent postoperative deep vein thrombosis, and improve surgical indicators and postoperative coagulation function. Also, it reduces the incidence of adverse reactions in patients and facilitates recovery. It has a beneficial impact on the prognosis of patients and deserves promotion.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".