A practical approach to evaluating postoperative thrombocytopenia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Identifying the cause(s) of postoperative thrombocytopenia is challenging. The postoperative period includes numerous interventions, including fluid administration and transfusion of blood products, medication use (including heparin), and increased risk of organ dysfunction and infection. Understanding normal thrombopoietin physiology and the associated expected postoperative platelet count changes is the crucial first step in evaluation. Timing of thrombocytopenia is the most important feature when differentiating causes of postoperative thrombocytopenia. Thrombocytopenia within 4 days of surgery is commonly caused by hemodilution and increased perioperative platelet consumption prior to thrombopoietin-induced platelet count recovery and transient platelet count overshoot. A much broader list of possible conditions that can cause late-onset thrombocytopenia (postoperative day 5 [POD5] or later) is generally divided into consumptive and destructive causes. The former includes common (eg, infection-associated disseminated intravascular coagulation) and rare (eg, postoperative thrombotic thrombocytopenic purpura) conditions, whereas the latter includes such entities as drug-induced immune thrombocytopenia or posttransfusion purpura. Heparin-induced thrombocytopenia is a unique entity associated with thrombosis that is typically related to intraoperative/perioperative heparin exposure, although it can develop following knee replacement surgery even in the absence of heparin exposure. Very late onset (POD10 or later) of thrombocytopenia can indicate bacterial or fungal infection. Lastly, thrombocytopenia after mechanical device implantation requires unique considerations. Understanding the timing and severity of postoperative thrombocytopenia provides a practical approach to a common and challenging consultation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it