Bibliographic record
Abstract
Bleeding manifestations in patients with immune thrombocytopenia (ITP) range from mild skin bruises to life-threatening intracranial hemorrhage (ICH). Severe bleeding is distinctly uncommon when the platelet count is >30 × 10(9)/L and usually only occurs when the platelet count falls <10 × 10(9)/L. Based on estimates from clinical studies, ITP registries and administrative databases, the frequency of ICH in patients with ITP is ~0.5% in children and 1.5% in adults. Estimates of severe (non-ICH) bleeding are difficult to obtain because of the lack of standardized case definitions; the lack of a universally accepted, ITP-specific bleeding assessment tool; and the omission of reporting bleeding outcomes in many clinical studies. In practice, the presence of bleeding should dictate whether or not treatment is needed because many patients, especially children, can be safely managed with observation alone. Guiding principles for the management of ITP, based on the bleeding risk are: (1) Decide when treatment is needed and when it can safely be withheld; (2) for patients with chronic ITP, use the least toxic treatment at the lowest dose; (3) emergency treatment of severe thrombocytopenia-associated bleeding requires combination therapy; and (4) early aggressive therapy may result in durable platelet count responses.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".