Continuous glycemia monitoring in perioperative period in patients undergoing total knee or hip arthroplasty: A protocol for a prospective observational study
Bibliographic record
Abstract
In every surgical subspecialty surgical site infection (SSI) or implant infections, although occur seldom, pose a threat to patients' health. Risk factors of such states is diabetes mellitus (DM), considered one of the most widespread health-related problems of the 21st century. Orthopedists perform big joint replacements that usually concern older adults and therefore often deal with patients suffering from comorbidities. DM is frequently one of them and can furthermore often remain underdiagnosed. The other risk for complication is a rapid beginning of the rehabilitation which starts on the day following the surgery. To eliminate the debilitating impact of DM and hypoglycemia on surgical patients, we aim to investigate the relationship between the glycemia values and the postoperative outcomes in certain periods of time in patients undergoing orthopedic surgeries. Participants meeting inclusion criteria will have inserted a glycemia measuring device (Dexcom G5, Inc., San Diego, CA) in the periods of time. First time it will take place 14 days prior to the surgery and right after the surgery for the second time for the period of another 14 days. All patients will undergo standard total knee arthroplasty or total hip arthroplasty procedures. Patients will be assessed preoperatively and 14 days, 1, 3, 6, 12, and 24 months postoperatively. The assessment of the joint condition will consist of: patient-reported outcomes (The Knee injury and Osteoarthritis Outcome Score, Harris Hip Score, the Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC]); assessment of potential SSI and cardiovascular complications (the Finnish Diabetes Risk Score [FINDRISC], the Systematic Coronary Risk Evaluation [SCORE]) and the clinical examination. To investigate the influence of orthopedic surgery (anesthesia) on glycemia and the significance and safety of early patients mobilization after the big joints surgeries. To investigate changes of glycemia in patients with normal glycemia metabolism, potentially protecting them from hypoglycemia during hospital stay and increasing their awareness of potential development of DM in the future. Additionally, this study will correlate perioperative glycemic levels with risk of cardiovascular events in one year follow-up, and its influence on SSI and implant complications.
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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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".