Protocol of a Multicenter Cohort Observational Study of Cerebral Venous Thrombosis in China Mainland
Bibliographic record
Abstract
Epidemiological data on cerebral venous thrombosis in China are still lacking at present on the aspects of incidence, recurrence, risk factors, and so on. Herein, we aimed to fill the gap, based on the result of this multicenter prospective cohort study. A total of 26 top tertiary hospitals distributed in China Mainland will take part in this study. For the first time, a dataset of cerebral venous thrombosis cohort (including multiethnic patients of all ages in almost all regions of Mainland China, regardless of gender) will be built. Inclusion criteria were as follows: (1) aged ≥14 years, (2) neuroimaging-confirmed cerebral venous thrombosis, (3) symptom onset was within 30 days prior to enrollment, (4) signed the informed consent form. Demographic data, risk factors, clinical and neuroimaging features, ophthalmologic and aural results, blood tests, cerebrospinal fluid examination, therapeutic strategies, and adverse events were analyzed. Two milliliters of fasting venous blood and 2 mL of cerebrospinal fluid will be collected and stored. Furthermore, patients will be followed up at months 1, 3, 6, and 12 after baseline assessment. Primary outcome will be all-cause mortality. Secondary outcomes: (1) cerebrospinal fluid pressure and Frisen grade; (2) recanalization rate on imaging; (3) rating scales such as GCS, NIHSS, mRS, Mini-Mental State Examination, Montreal Cognitive Assessment, Patient Health Questionnaire 9-item, HIT-6, and Tinnitus Handicap Index. This study will for the first time provide strong evidence on the incidence rate, recurrence rate, and demographic data, as well as special risk factors, clinical outcomes, symptomatic and imaging features of cerebral venous thrombosis in Chinese population. The results of this study will also provide an important reference on prevention, early diagnosis, and customized treatment of cerebral venous thrombosis in Chinese patients.
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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".