Preoperative evaluation in infants and children: recommendations of the Italian Society of Pediatric and Neonatal Anesthesia and Intensive Care (SARNePI).
Bibliographic record
Abstract
BACKGROUND: The preoperative assessment involves the process of evaluating the patient's clinical condition, which is intended to define the physical status classification, eligibility for anesthesia and the risks associated with it, thus providing elements to select the most appropriate and individualized anesthetic plan. The aim of this recommendation was provide a framework reference for the preoperative evaluation assessment of pediatric patients undergoing elective surgery or diagnostic/therapeutic procedures. METHODS: We obtained evidence concerning pediatric preoperative evaluation from a systematic search of the electronic databases MEDLINE and Embase between January 1998 and February 2012. We used the format developed by the Italian Center for Evaluation of the Effectiveness of Health Care's scoring system for assessing the level of evidence and strength of recommendations. RESULTS: We produce a set of consensus guidelines on the preoperative assessment and on the request for preoperative tests. A review of the existing literature supporting these recommendations is provided. In reaching consensus, emphasis was placed on the level of evidence, clinical relevance and the risk/benefit ratio. CONCLUSION: Preoperative evaluation is mandatory before any diagnostic or therapeutic procedure that requires the use of anesthesia or sedation. The systematic prescription of complementary tests in children should be abandoned, and replaced by a selective and rational prescription, based on the patient history and clinical examination performed during the preoperative evaluation.
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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 | 0.028 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".