MétaCan
Menu
Back to cohort
Record W3125072944 · doi:10.1097/md.0000000000024213

Efficacy of minimally invasive surgery for the treatment of hypertensive intracerebral hemorrhage

2021· article· en· W3125072944 on OpenAlexaff
Jiang Liu, Jing Cheng, Hongjun Zhou, Chunyan Deng, Zhengxin Wang

Bibliographic record

VenueMedicine · 2021
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsMedicineIntracerebral hemorrhageGlasgow Coma ScaleHematomaSurgeryRandomized controlled trialStroke (engine)ComplicationIntracranial pressureAnesthesiaInvasive surgeryCerebral edema

Abstract

fetched live from OpenAlex

INTRODUCTION: Hypertensive intracerebral hemorrhage (HICH) is the most serious complication of hypertension. Clearing intracranial hematoma as soon as possible, reducing brain cell edema, and controlling intracranial pressure could effectively reduce neuron damage, lower patient mortality, and improve patient prognosis. At present, minimally invasive surgery (MIS) has been widely used and plays an important role in the treatment of HICH. However, it is still in controversies about the choice of surgical treatment and medication treatment for HICH. Therefore, we try to conduct a randomized, controlled, prospective trial to observe the efficacy of MIS treatment against HICH compared with medication treatment. METHODS: Patients will be randomly divided into treatment group and control group in a 1:1 ratio using the random number generator in Microsoft Excel. Stereotactic soft channel minimally invasive intracranial hematoma puncture and drainage treatment and medication treatment will be applied respectively. The outcomes of intracerebral hemorrhage volume, Glasgow coma scale, National Institutes of Health Stroke Scale will be recorded. CONCLUSIONS: The findings of the study will be helpful for the choice of MIS and conservative treatment when treating HICH patients. TRIAL REGISTRATION: OSF Registration number: DOI 10.17605/OSF.IO/ME6Y5.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.048
GPT teacher head0.308
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueMedicineSame topicIntracerebral and Subarachnoid Hemorrhage ResearchFrench-language works237,207