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Record W3139380762 · doi:10.1136/svn-2020-000656

Predicting long-term outcomes in acute intracerebral haemorrhage using delayed prognostication scores

2021· article· en· W3139380762 on OpenAlexaff
Ronda Lun, Vignan Yogendrakumar, Tim Ramsay, Michel Shamy, Robert Fahed, Magdy Selim, Dar Dowlatshahi

Bibliographic record

VenueStroke and Vascular Neurology · 2021
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineModified Rankin ScaleReceiver operating characteristicIntracerebral hemorrhageInternal medicineArea under the curve

Abstract

fetched live from OpenAlex

OBJECTIVE: The concept of the 'self-fulfilling prophecy' is well established in intracerebral haemorrhage (ICH). The ability to improve prognostication and prediction of long-term outcomes during the first days of hospitalisation is important in guiding conversations around goals of care. We previously demonstrated that incorporating delayed imaging into various prognostication scores for ICH improves the predictive accuracy of 90-day mortality. However, delayed prognostication scores have not been used to predict long-term functional outcomes beyond 90 days. DESIGN, SETTING AND PARTICIPANTS: We analysed data from the ICH Deferoxamine trial to see if delaying the use of prognostication scores to 96 hours after ICH onset will improve performance to predict outcomes at 180 days. 276 patients were included. INTERVENTIONS AND MEASUREMENTS: We calculated the original ICH score (oICH), modified-ICH score (MICH), max-ICH score and the FUNC score on presentation (baseline), and on day 4 (delayed). Outcomes assessed were mortality and poor functional outcome in survivors (defined as modified Rankin Scale of 4-5) at 180 days. We generated receiver operating characteristic curves, and measured the area under the curve values (AUC) for mortality and functional outcome. We compared baseline and delayed AUCs with non-parametric methods. RESULTS: At 180 days, 21 of 276 (7.6%) died. Out of the survivors, 54 of 255 had poor functional outcome (21.2%). The oICH, MICH and max-ICH performed significantly better at predicting 180-day mortality when calculated 4 days later compared with their baseline equivalents ((0.74 vs 0.83, p=0.005), (0.73 vs 0.80, p=0.036), (0.74 vs 0.83, p=0.008), respectively). The delayed calculation of these scores did not significantly improve our accuracy for predicting poor functional outcomes. CONCLUSION: Delaying the calculation of prognostication scores in acute ICH until day 4 improved prediction of 6-month mortality but not functional outcomes. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Registry (NCT02175225).

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.309
Teacher spread0.284 · 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 designObservational
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

Citations13
Published2021
Admission routes1
Has abstractyes

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