Recommendations for optimal ICD codes to study neurologic conditions
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
OBJECTIVE: Administrative health data are frequently used for large population-based studies. However, the validity of these data for identifying neurologic conditions is uncertain. METHODS: This article systematically reviews the literature to assess the validity of administrative data for identifying patients with neurologic conditions. Two reviewers independently assessed for eligibility all abstracts and full-text articles identified through a systematic search of Medline and Embase. Study data were abstracted on a standardized abstraction form to identify ICD code-based case definitions and corresponding sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs). RESULTS: Thirty full-text articles met the eligibility criteria. These included 8 studies for Alzheimer disease/dementia (sensitivity: 8-86.5, specificity: 56.3-100, PPV: 60-97.9, NPV: 68.0-98.9), 2 for brain tumor (sensitivity: 54.0-100, specificity: 97.0-99.0, PPV: 91.0-98.0), 4 for epilepsy (sensitivity: 98.8, specificity: 69.6, PPV: 62.0-100, NPV: 89.5-99.1), 4 for motor neuron disease (sensitivity: 78.9-93.0, specificity: 99.0-99.9, PPV: 38.0-90.0, NPV: 99), 2 for multiple sclerosis (sensitivity: 85-92.4, specificity: 55.9-92.6, PPV: 74.5-92.7, NPV: 70.8-91.9), 4 for Parkinson disease/parkinsonism (sensitivity: 18.7-100, specificity: 0-99.9, PPV: 38.6-81.0, NPV: 46.0), 3 for spinal cord injury (sensitivity: 0.9-90.6, specificity: 31.9-100, PPV: 27.3-100), and 3 for traumatic brain injury (sensitivity: 45.9-78.0 specificity: 97.8, PPV: 23.7-98.0, NPV: 99.2). No studies met eligibility criteria for cerebral palsy, dystonia, Huntington disease, hydrocephalus, muscular dystrophy, spina bifida, or Tourette syndrome. CONCLUSIONS: To ensure the accurate interpretation of population-based studies with use of administrative health data, the accuracy of case definitions for neurologic conditions needs to be taken into consideration.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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