P.166 Rate and clinical utility of early postoperative CT head in adult craniotomy
Bibliographic record
Abstract
Background: Routine early postoperative CT (EPCT) in neurologically intact patients is unsupported but still common. Recent studies suggest 135 scans are needed to detect one clinically silent abnormality (Blumrich, 2021). This study assessed the rate and utility of EPCT, defined as a CT head scan within 24 hours of adult craniotomy. Methods: Retrospective review of adult craniotomy cases at the University of Alberta Hospital was conducted over a 10-month period. Data on EPCT rates, timing, adverse findings (complication or unfavourable outcome, e.g., bleeding, extensive pneumocephalus, edema, ischemia), as well as clinical data on neurologic deterioration (e.g., weakness, aphasia, visual impairment, decreased LOC), and repeat surgical interventions were extracted. Results: Of 405 patients (200 female, 54.6 ± 0.8 years, range: 19-89), 96.5% (391/405) underwent EPCT. Adverse EPCTs occurred in 9.2% (36/391), with neurologic deterioration in 7.7% (30/391) and repeat surgery in 2.8% (11/391). Adverse scans and neurologic deterioration were strongly correlated (X2=141.1, p=1.54e-32). Only 0.5% (2/405) of EPCT findings prompting surgery lacked prior neurologic deterioration. Conclusions: EPCT in the absence of neurologic deterioration has a low yield for surgical intervention and may be safely omitted.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".