Chronic Subdural Hematoma: Toward a New Management Paradigm for an Increasingly Complex Population
Bibliographic record
Abstract
Chronic subdural hematoma (cSDH) is a frequent yet poorly studied entity. Patients with cSDH are increasingly using antithrombotic medication, are now older, and present with a variety of clinical symptoms, including incidental discoveries. Despite this increasing complexity, management has remained roughly unchanged since the late 1990s. We review here the state of cSDH research under way at Université de Sherbrooke and around the world with a focus on studies addressing specific gaps in the current evidence base. We show that evidence is lacking at many decision points in the typical cSDH patient treatment algorithm. No definition of cSDH is universally accepted, and a formal definition project, along with suggested common data elements to be reported in future trials (CODE-CSDH: formal cSDH definition project) is ongoing. An amendment to International Statistical Classification of Diseases and Related Health Problems (ICD-11) has also been proposed to improve classification and registry research. Within the cSDH clinical assessment, evidence for the occurrence of nonepileptic, stereotypical, and intermittent symptoms (NESIS) is emerging. The GENESIS study (Generation Evidence on the etiology and management of NESIS) will test etiological and therapeutic hypotheses for this patient subpopulation. For patients at high risk of recurrence, the TRACS (TXA for cSDH) and EMMACS studies (Embolization of the Middle Meningeal Artery in Chronic Subdural Hematoma study) are, respectively, assessing the use of tranexamic acid and meningeal artery embolization. The overarching vision is that patients with cSDH might be stratified for operative versus conservative treatment based on the need for mass effect removal, then be offered adjuvant therapies based on their risk of recurrence and thrombotic complications. We believe that such tailoring of therapy to each individual should help improve outcomes.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.011 |
| 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".