Obstetric Neuraxial Anesthesia in the Context of Immune Thrombocytopenia: A Systematic Review [20F]
Bibliographic record
Abstract
INTRODUCTION: Immune Thrombocytopenia (ITP) is characterized by preserved platelet function. The true magnitude of neuraxial hematoma risk in ITP parturients is unknown, yet obstetric neuraxial anesthesia (OBNA) is often withheld. The aim of this systematic review was to examine OBNA outcomes in ITP with platelet counts below 100x10(9)/L. METHODS: Relevant literature database-entries were searched to May 2018. Citations were independently screened and extracted in duplicate. Risk of bias was assessed using Newcastle-Ottawa Scale, NIH Case Series Assessment Tool and Joanna Briggs Institute Case Reports Checklist, as appropriate. Study authors were contacted for additional information. RESULTS: Our search yielded 954 titles/abstracts for screening with 62 full-texts reviewed, 26 included, and individual patient data obtained for 9. Of 291 individuals, 166 received OBNA; 61 at platelet counts below 80x10(9)/L. No neuraxial hematomas were reported. Median platelet counts were 80-93x10(9)/L. In a meta-analysis of 6 studies, platelets were higher in those with OBNA than those without [mean difference (MD), (95% confidence interval) 19 (3, 25) x10(9)/L; p<0.00001], but did not differ between epidural and spinal anesthesia [MD 0.4 (-14, 20) x10(9)/L; p=0.86]. The “rule of three” places the upper bound of the 95% confidence interval for neuraxial hematoma risk at 1.8%. CONCLUSION: Our study further supports the quest to establish safety of OBNA in ITP at lower platelet counts. It highlights the reluctance to offer OBNA below the 80x10(9)/L threshold suggested by guidelines, yet based largely on theoretical presumption of risk. Given the rarity of OBNA in ITP at progressively lower platelet counts, an international registry could prove indispensable.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".