Self ratings of health predict functional outcome and recurrence free survival after stroke
Bibliographic record
Abstract
STUDY OBJECTIVE: To measure stroke victims' self rated health (SRH) status and SRH transition, and to compare how the two are prospectively associated with disability and recurrence free survival. DESIGN: Prospective case registry study with face to face follow up interviews at three months, one, two, and three years. Ascertained were SRH status and SRH transition using single question assessments, Barthel Index (BI), Frenchay Activities Index (FAI), and Mini Mental State Examination (MMSE). SETTING: A multiethnic inner city population of 234 533. PARTICIPANTS: Patients surviving the initial three months after a first in a lifetime stroke in 1995 to 1998. RESULTS: Of 690 stroke survivors 561 (81.3%) could complete the self report items. Answers to the item on SRH status did not vary significantly between the four follow up interviews. However, responses to the item on SRH transition changed significantly during follow up with three months ratings being more negative than all subsequent ratings. SRH transition, but not SRH status, showed a prospective association with long term outcome in multivariate analyses controlling for the BI, FAI, and MMSE. Compared with all other patients, patients reporting "Much worse health" at three months were more likely to be disabled ( = BI<20) at one year (OR 6.29, 95% CI 2.26 to 17.52) and their combined risk of stroke recurrence and death was increased over five years (HR 1.72, 95% CI 1.25 to 2.38). CONCLUSIONS: Items on SRH should be used with caution in populations with high rates of disability and language problems, as many participants are unable to complete them. SRH transition may be a better predictor of disability and recurrence free survival after major medical events than SRH status.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".