Bibliographic record
Abstract
SIR—Medications, including anticholinergics [1, 2], neuroleptics and narcotics [3], are recognised as contributing factors to delirium. Often, these medications are prescribed on a PRN (as needed) basis for management of post-operative nausea, delirium and pain. Additionally, antipsychotics and benzodiazepines are the prescribed PRN for delirium symptoms. Delirium complicates the assessment of pain because of the overlapping symptoms and impaired communication, thus resulting in inadequate treatment of pain in delirious older patients [4]. Poor pain control may contribute to worsening cognition and a cycle of ineffective management. The desire to improve delirium management resulted in a geriatric service nurse practitioner and pharmacist from a large Canadian hospital being invited to participate in a one-day session on education for orthopaedic nurses. The purpose of this study is to evaluate one of the outcomes of the educational intervention program: use of PRN medications following repair of hip fracture or elective hip arthroplasty. Little is known regarding the process of clinical decision-making among nurses with regard to administering PRN medications, although physicians and nurses may have different approaches. In a psychiatric setting, nurses and physicians had disparate views on the use of PRN medications, including the use of antipsychotics and benzodiazepines [5]. Using simulations of analgesics needed in post-operative cancer patients, Di Giulio and Crow reported a non-statistically significant difference in the amount of patient information collected by the two disciplines during assessment [6]. Differences between the medication prescriber (physician) and administrator (nurse) have the potential to adversely affect patient care. There is an absence of evidence that can be used to base the clinical use of PRN psychotropic medication in mental health settings [7], and we suggest that this situation exists with regard to delirious older adults as well. Nurses are called upon to manage complex and overlapping symptoms, pain and delirium, often with little guidance for practice.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.241 | 0.107 |
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".