Synoptic operative reports for spinal cord injury patients as a tool for data quality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The advent of synoptic operative reports has revolutionized how clinical data are captured at the time of care. In this article, an electronic synoptic operative report for spinal cord injury was implemented using interoperable standards, HL7 and Systematized Nomenclature of Medicine-Clinical Terms. Subjects (N = 10) recruited for a pilot study completed recruitment and feedback questionnaires, and produced both an electronic synoptic operative report for spinal cord injury report and a dictated narrative operative report for an actual patient case. Results indicated heterogeneity by subjects in access and use of electronic sources of patient data. Feedback questionnaire results confirmed that subjects were comfortable using both methods for data entry of operative reports, and that some were unable to find the diagnosis terms they needed in electronic synoptic operative report for spinal cord injury. Data quality improved. Electronic synoptic operative report for spinal cord injury reports were more complete (95.26%) than dictated (80%) for all subjects. An accuracy assessment, which considered usability for secondary data use, was conducted and the electronic synoptic operative report for spinal cord injury was demonstrated to improve accuracy.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it