RELIABILITY OF PRACTICAL IRANIAN CRITERIA (PIC) FOR CLASSIFICATION OF BRAIN INFARCT
Bibliographic record
Abstract
Background: Various classification criteria of brain infarct are used in clinical trials and stroke registries. The practical Iranian criteria (PIC) is designed for clinical practice. Methods: From March 2001 through March 2003, all consecutive stroke patients admitted to Vali-e- Asr Hospital, Birjand, Khorasan, Iran were included in this prospective observational study. Patients underwent a standard battery of diagnostic investigations by a stroke neurologist. Data on patients, demographics, clinical presentations, and diagnostic work-up were kept in a database. Two stroke neurologists and a general practitioner independently reviewed the data of 20 randomly selected patients and classified patients according to the PIC classification of stroke topography and etiology. The PIC is designed by stroke neurologists and approved in the University of Alberta, Canada. The degrees of interrater agreement were measured with unweighted k-statistics. Results: Among 302 stroke patients, 20 patients (11 females, 9 males) were randomly selected. The three interrater agreement for topographic subtyping of the patients was 0.95%, k = 0.915 (0.662 – 1), P < 0.0001 and for etiologic diagnosis was 0.90 %; k = 0.9022 (0.753 – 1), P < 0.0001. Stroke neurologists agreed in topographic diagnosis for 20 out of the 20 cases (100%; k = 1; 95% CI, 1.0 to 1.0; P < 0.0001). The general practitioner arrived at the same topographic diagnosis for 19
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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 | 0.029 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".