Association of Symptomatic Hearing Loss with Functional and Cognitive Recovery 1 Year after Intracerebral Hemorrhage
Bibliographic record
Abstract
Survivors of intracerebral hemorrhage (ICH) are at high risk for poor functional and cognitive outcomes.At 12 months from the acute hemorrhage less than a third achieve functional independence, while over 25% are diagnosed with dementia and many more report milder cognitive deficits.[1][2][3] Hearing loss represents modifiable risk factor for functional decline cognitive dysfunction, yet it is often underdiagnosed and insufficiently addressed among individuals at risk. 4 We therefore sought to quantify the incidence of hearing loss among ICH survivors, identify associated risk factors, and determine whether it is associated with poor neurological recovery.We analyzed data for consecutive patients admitted to Massachusetts General Hospital between January 1st 2006 and December 31st 2017 with a spontaneous ICH diagnosis.5 Admission CT scans were analyzed to determine ICH location and hematoma volume. 2 We used validated ordinal scales to evaluate overall cerebral small vessel disease, cerebral amyloid angiopathy (CAA), and hypertensive arteriopathy burden on brain magnetic resonance imaging (MRI) scans obtained according to a previously validated protocol.1 We initially screened for diagnosis of hearing loss by analyzing participants' electronic health records (EHR) using a natural language processing approach.6 All hearing loss diagnoses were then confirmed by manual review of EHR.We captured information on functional performance status on the modified Rankin Scale (mRS) at dis-
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".