Specific clinical findings, including coma, neck stiffness and seizures, increase the likelihood of haemorrhagic stroke, but no combination of features is definitively diagnostic
Bibliographic record
Abstract
Commentary on: 1. Runchey S, 2. McGee S . Does this patient have a hemorrhagic stroke?: clinical findings distinguishing hemorrhagic stroke from ischemic stroke. JAMA 2010;303:2280–6. [OpenUrl][1][CrossRef][2][PubMed][3] Stroke presentations should be conceptualised as stroke syndromes, much in the same way that we now think about acute coronary syndromes. The clinical manifestations reflect brain dysfunction, but not necessarily the underlying cause. Ischaemic stroke is the most common stroke type, comprising 65–85% of all stroke, varying by location in the world. Ischaemic stroke is potentially treatable with systemic and endovascular thrombolysis, or minimally with antithrombotic medication (ie, ASA). In contrast, such treatment is inappropriate in the hyperacute setting for the two main haemorrhagic forms of stroke – intracerebral haemorrhage (ICH) and subarachnoid haemorrhage. In the emergency evaluation of patients, it is, therefore, critical to know whether the patient has an ischaemic or haemorrhagic stroke. The definitive way to know this is brain imaging, conventionally with CT or MR. With rare exceptions, both CT and MR are fixed resources located at hospitals. The availability of such imaging is widespread in most … [1]: {openurl}?query=rft.jtitle%253DJAMA%26rft.stitle%253DJAMA%26rft.issn%253D0002-9955%26rft.aulast%253DRunchey%26rft.auinit1%253DS.%26rft.volume%253D303%26rft.issue%253D22%26rft.spage%253D2280%26rft.epage%253D2286%26rft.atitle%253DDoes%2BThis%2BPatient%2BHave%2Ba%2BHemorrhagic%2BStroke%253F%253A%2BClinical%2BFindings%2BDistinguishing%2BHemorrhagic%2BStroke%2BFrom%2BIschemic%2BStroke%26rft_id%253Dinfo%253Adoi%252F10.1001%252Fjama.2010.754%26rft_id%253Dinfo%253Apmid%252F20530782%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1001/jama.2010.754&link_type=DOI [3]: /lookup/external-ref?access_num=20530782&link_type=MED&atom=%2Febmed%2F15%2F6%2F183.atom
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.050 | 0.034 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".