Bibliographic record
Abstract
Background: Studies in the literature suggest preoperative laboratory investigations and cross-match are performed unnecessarily and rarely lead to changes in clinical management. This study explored whether preoperative laboratory investigations in neurosurgical children alter clinical management and to determine the utilization of cross-matched blood perioperatively in elective pediatric neurosurgical cases. Methods: We reviewed patient charts for elective neurosurgery procedures (2010-2014) at our institution. Variables collected include preoperative complete blood count (CBC), electrolytes, coagulation, group and screen, and cross-match. Instances of altered clinical management as a consequence of preoperative investigation were noted. The number of cross-matched blood transfused perioperatively was also determined. Results: 477 electively scheduled pediatric neurosurgical patients were reviewed. Preoperative CBC was done on 294 and 39.8% had at least one laboratory abnormality. Electrolytes and coagulation panels were abnormal in 23.8% and 24.5% respectively. The preoperative investigations led to a change in clinical management in three patients, two of which were associated with significant past medical history. 57.9% had blood cross-matched and 3.6% of patients received perioperative blood transfusions. The cross-match to transfusion ratio was 16. Conclusion: This study suggests that the results of preoperative laboratory exams have limited value, apart from cases with oncology and complex pre-existing conditions. Additionally, cross-matching might be excessively conducted in elective pediatric neurosurgical cases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".