Safety of lumbar punctures in patients with thrombocytopenia
Bibliographic record
Abstract
BACKGROUND: American Association of Blood Banks guidelines recommend a minimum platelet count of 50 × 10(9) /l for LPs (lumbar puncture), but evidence is lacking. The objective of this study was to describe the range of platelet counts at which LPs are performed, and the rate of traumatic taps and haemorrhagic complications in an adult oncology population. METHODS: A retrospective cohort study of patients receiving LPs over a 2-year period was carried out. Bleeding risk factors captured included anticoagulants, antiplatelets, end-stage renal disease, and other bleeding disorders. Pre-LP platelet counts were those collected ≤24 h from the time of the LP. Traumatic tap was defined as 500 or more red blood cells per high-power field in cerebrospinal fluid. RESULTS: One hundred and thirty-five patients underwent 369 LPs. Twenty-eight (7·6%) LPs were performed at a platelet count ≤ 50 × 10(9) /l; 18 patients received a platelet transfusion prior to the LP, with post-transfusion count available prior to LP in only one patient. Traumatic taps occurred in 16 of 113 (14·2%) LPs in patients with thrombocytopenia (platelet count < 150 × 10(9) /l) compared to 27 of 242 (11·1%) LPs in patients with a normal platelet count (P = 0·48). The presence of bleeding risk factors did not increase the incidence of traumatic taps. There were no haemorrhagic complications. CONCLUSIONS: Among this cohort of adult oncology patients, there were no haemorrhagic complications. Traumatic taps were not increased in patients with thrombocytopenia. The effects of platelet transfusions were rarely assessed prior to LP. Further studies should be pursued to assess whether platelet count thresholds lower than 50 × 10(9) /l are safe for lumbar puncture.
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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 | 0.019 |
| 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 | 0.001 |
| 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".