The Implementation of a Postoperative Care Process on a Neurosurgical Unit
Bibliographic record
Abstract
The postoperative phase is a critical time for any neurosurgical patient. Historically, certain patients having neurosurgical procedures, such as craniotomies and other more complex surgeries, have been nursed postoperatively in the intensive care unit (ICU) for an overnight stay, prior to transfer to a neurosurgical floor. At the Hospital for Sick Children in Toronto, because of challenges with access to ICU beds and the cancellation of surgeries because of lack of available nurses for the ICU setting, this practice was reexamined. A set of criteria was developed to identify which postoperative patients should come directly to the neurosurgical unit immediately following their anesthetic recovery. The criteria were based on patient diagnosis, preoperative condition, comorbidities, the surgical procedure, intraoperative complications, and postoperative status. A detailed process was then outlined that allowed the optimum patients to be selected for this process to ensure patient safety. Included in this process was a postoperative protocol addressing details such as standard physician orders and the levels of monitoring required. Outcomes of this new process include fewer surgical cancellations for patients and families, equally safe, or better patient care, and the conservation of limited ICU resources. The program has currently been expanded to include patients who have undergone endovascular therapies.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".