Ability of hemostatic assessment to detect bleeding disorders and to predict abnormal surgical blood loss in children: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Systematic preoperative coagulation testing is still widely used in children scheduled for surgery, although current guidelines recommend that a bleeding history should be the first choice for hemostatic assessment. We performed a systematic review with meta-analysis to evaluate the pertinence of bleeding questionnaire and screening laboratory testing to detect bleeding disorders (BDs) in children and to predict abnormal surgical blood loss. METHODS: A search was conducted in PubMed, EMBASE, MEDLINE(R), Cochrane Central Register of Controlled Trials, Health technology Assessment, and all EBM Reviews (Cochrane DSR, ACP Journal Club, DARE, CCTR, CMR, HTA, and NHSEED and EBM Reviews) up to October 22, 2013. Prospective trials containing 20 children or more and any tests evaluating either the ability of the test to detect a congenital BD or the ability of the test to predict increased surgical blood loss were retained. The quality of the study was judged with the Cochrane Collaboration Tool and two investigators extracted data independently. Data were combined to calculate the pooled diagnostic odds ratio (DOR) and their 95% confidence intervals (CI 95%). I(2) statistics were used to assess statistics heterogeneity. RESULTS: Data could be extracted from 16 studies. Best results for detecting a congenital abnormality at potential risk for increased surgical blood loss were obtained with the PFA-100 (DOR = 113.0; 95% CI, 22.6-566.2; I(2) = 0%) in two studies, followed by the bleeding time in two other studies (DOR = 110.7; 95% CI, 24.4-502.3; I(2) = 0%). With a high amount of heterogeneity, questionnaires showed disappointing performances (DOR = 7.9; 95% CI: 3.5-17.5; I(2) = 72.6%). CONCLUSION: Current evidence does not identify a tool that adequately predicts BDs and/or abnormal surgical blood loss in children. Questionnaires currently available do not perform well. In the setting of a pediatric coagulation clinic, the PFA-100 has the highest chance of detecting a BD. This meta-analysis highlights the weakness of the literature regarding the prediction of perioperative bleeding due to congenital hemostatic disorders in children.
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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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".