Interprofessional management of a complex continuing care patient admitted with 18 pressure ulcers: a case report.
Bibliographic record
Abstract
Interprofessional practice (IP)--ie, collaborative practice--involves interaction and knowledge-sharing between professionals from different disciplines in order to meet the needs of the patient. This approach to care is well suited to patients with pressure ulcers, whose complex and varying presentations require the monitoring and consultation of an IP team. A 44-year-old man with anoxic brain injury was admitted to a complex continuing care facility with 18 wounds, 17 of which were pressure ulcers. The patient was at high risk for further skin breakdown as a result of immobility, incontinence, impaired cognition, impaired sensation, low body weight, and positioning challenges secondary to contractures and spasticity. Wounds were located primarily around the patient's sacrum, trochanters, feet, and ankles. The care team included a physician, unit manager, clinical nurse educator, nurses, physiotherapist, occupational therapist, registered dietician, and pharmacist, all with varying roles related to wound care. The patient's wife was concerned about his overall health status and wanted to move him out of his room in a wheelchair to spend time with him. Using current best practices, the IP team implemented management strategies that facilitated wheelchair time during family visits; plus, all 18 wounds healed within 15 months of admission. The patient did not develop any new areas of skin breakdown. IP collaboration facilitated the problem-solving needed to meet the complex needs of this patient.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".