Reliability associated with the abstraction of data from medical records for inclusion in an information system for persons with a traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: This article presents the intra- and inter-rater reliability associated with the extraction, from medical rehabilitation charts, of data to be included in a head injury information system currently under development. METHODS: A data collection form was developed to facilitate and standardize the data extraction. Two clinicians extracted information pertaining to 231 variables of the system from 15 charts of persons receiving rehabilitation services following a head injury. RESULTS: Average percentage agreement was high and did not vary from one category of variables to the other (84-88%). Substantial intra-rater agreement (kappa = 0.66) and moderate inter-rater agreement (kappa = 0.56) were found to be associated with the extraction of the variables studied. CONCLUSIONS: The results suggest that clinicians using standardized procedures can reliably extract important data pertaining to personal history, impairments, and disabilities relating to sensorimotor function. Some potential sources of error are identified and recommendations are presented.
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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 | 0.095 | 0.323 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".