Electronic Case-Report Forms of Symptoms and Impairments of Peripheral Neuropathy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: For the conduct of controlled clinical trials, epidemiologic surveys or even of medical practice of varieties of peripheral neuropathy, the usefulness, error rate and cost-effectiveness of scannable case-report forms has not been studied. MATERIALS AND METHODS: The overall performance, the frequency of the problems identified and corrected, and the time saved from use of a standard paper case report form was evaluated in multicenter treatment trials, single center epidemiologic surveys and in our neurologic practice. The paper case report form (Clinical Neuropathy Assessment [CNA]) for pen entry at study medical centers for patient, disease and demographic information (Lower Limb Function [LLF] and Neuropathy Impairment Score [NIS]) can be faxed to a core Reading and Quality Assurance Center where the form and data is electronically and interactively evaluated and corrected, if needed, by participating medical centers before electronic entry into database. OBSERVATIONS AND CONCLUSIONS: 1) The approach provides a standard, scannable paper case report form for pen entry of neuropathy symptoms, impairments and disability at the bedside or in the office which is retained as a source document at the participating medical center but a facsimile can be transferred instantaneously, its data can be programmed, interactively evaluated, modified and stored while maintaining an audit trail; 2) it allowed efficient and accurate reading, transfer, analysis, and storage of data of more than 15,000 forms used in multicenter trials; 3) in 500 consecutive CNA evaluations, software programs identified and facilitated interactive corrections of omissions, discrepancies, and disease and study inconsistencies, introducing only a few readily identified and corrected entry errors; and 4) use of programmed, as compared to non-programmed assessment, was more accurate than double keyboard entry of data and was approximately five times faster.
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.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".