Bleeding self‐assessments by patients with immune thrombocytopenia (ITP): An agreement study
Bibliographic record
Abstract
We designed anagreement study to compare the results of bleeding assessments done in tandem by ITP patients and trained research staff. We used a modified version of the ITP Bleeding Scale, which captured the patients' worst bleeding event at any of nine anatomical sites since the time of the last assessment. Interrater agreement was determined using the 2-way kappa for the assessment of severe vs. non-severe bleeds. We analyzed 108 consecutive patients with ITP from the McMaster ITP Registry who had duplicate bleeding assessments. Two-way agreement was excellent for gynecological (k = 0.86, 95% CI 0.71-1.02), gastrointestinal (k = 1), genitourinary (k = 1), pulmonary (k = 1) and intracranial (k = 1) bleeds; good for skin (k = 0.68, 95% CI, 0.54-0.82), oral (k = 0.76, 95% CI, 0.53-0.98) and ocular (k = 0.66, 95% CI, 0.04-1-28) bleeds; and moderate for epistaxis (k = 0.58, 95% CI, 0.21-0.95). Bleeding self-assessments by ITP patients were similar to trained research staff, but disagreements in severity grades were more frequent with skin bleeds, oral bleeds and epistaxis. Bleeding self-assessments could simplify bleeding assessments in clinical trials.
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 | 0.031 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".