Platelet transfusions and thrombocytopenia in intensive care units: Protocol for an international inception cohort study (PLOT‐ICU)
Bibliographic record
Abstract
Abstract Introduction Thrombocytopenia is frequent in intensive care unit (ICU) patients and has been associated with worse outcome. Platelet transfusions are often used in the management of ICU patients with severe thrombocytopenia. However, the reported frequencies of thrombocytopenia and platelet transfusion practices in the ICU vary considerably. Therefore, we aim to provide contemporary epidemiological data on thrombocytopenia and platelet transfusion practices in the ICU. Methods We will conduct an international inception cohort, including at least 1000 acutely admitted adult ICU patients. Routinely available data will be collected at baseline (ICU admission), and daily during ICU stay up to a maximum of 90 days. The primary outcome will be the number of patients with thrombocytopenia (a recorded platelet count < 150 × 109/L) at baseline and/or during ICU stay. Secondary outcomes include mortality, days alive and out of hospital, days alive without life‐support, the number of patients with at least one bleeding episode, at least one thromboembolic event and at least one platelet transfusion in the ICU, the number of platelet transfusions and the indications for transfusion. The primary and secondary outcomes will be presented descriptively. In addition, we will assess risk factors for developing thrombocytopenia during ICU stay and the association between thrombocytopenia at baseline and 90‐day mortality using logistic regression analyses. Conclusion The outlined international PLOT‐ICU cohort study will provide contemporary epidemiological data on the burden and clinical significance of thrombocytopenia in adult ICU patients and describe the current platelet transfusion practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".