Device or Ice: The Effect of Consistent Cooling Using a Device Compared with Intermittent Cooling Using an Ice Bag after Total Knee Arthroplasty
Bibliographic record
Abstract
Purpose : To determine the comparative effectiveness of consistent cooling using an icing device (DonJoy Iceman, DJO Canada, Mississauga, ON) versus intermittent cooling using an ice bag (usual care) for the first 48 hours after total knee arthroplasty (TKA). Method : A sample of 78 patients (intervention group, n=37; control group, n=34) undergoing primary TKA were randomized to intervention (device) or control (ice) groups. The primary outcome was pain intensity, measured by numerical pain rating scale (NPRS). Secondary outcomes were passive range of motion (PROM), nausea or vomiting, opioid use, blood loss, lower limb function, hospital length of stay, and patient-reported compliance and satisfaction. Results : No significant differences in the primary outcome (pain intensity measured via NPRS) were observed between control and intervention groups. Patients in the intervention group were significantly more satisfied (8.4 vs. 6.0, p=0.002); used the device more consistently, day and night (85.7% vs. 29.6% and 87.6% vs. 30.8%, respectively, p<0.001); and were more likely to recommend this method of cooling (96.8% vs. 68.0%, p=0.004). Conclusion : The study found no additional benefit of consistent cryotherapy using the icing device over intermittent ice bags on postoperative pain, PROM, nausea or vomiting, opioid use, blood loss, lower limb function, or length of stay, despite significant differences in patient-reported compliance and satisfaction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".