Validity and reliability of external cause injury International Classification of Diseases, Tenth Revision (ICD-10) codes: a systematic review
Bibliographic record
Abstract
v e T h i s systematic review is examining the following research question for populations of all ages in all countries: what is the validity and reliability of the International Classification of Diseases, Tenth Revision (ICD-10) codes for external-cause injuries?Rationale The International Classification of Diseases (ICD) codes are used worldwide in all areas of healthcare as a coding system to report diagnoses in hospital databases, including classifying diagnoses for various diseases, disorders, injuries, and other health conditions, symptoms, and procedures.In addition to being a coding diagnostic reporting system, they may be used for billing purposes, claims processing, medical care review, classifying data, and for healthcare statistics reporting [1].The International Classification of Diseases is the most widely used classification system for hospital records, and approximately 70% of global health expenditure is distributed according to their data [2,3].Therefore, accurate reporting of these codes is essential for m a i n t a i n i n g h i g h -q u a l i t y h e a l t h c a re d a t a worldwide.The 10th revision of ICD codes (called ICD-10) was developed by the World Health Organization (WHO) and are currently used worldwide [2,3].T h e s e c o d e s h a v e b e e n i n e ff e c t s i n c e approximately year 2000, though this varies by country.Despite their wide use in healthcare, the overall validity and reliability of these codes for external-cause injuries has yet to be examined.Individual studies have reported their validity and reliability for different types of injuries, but an overall analysis of the ICD-10 codes' accuracy to diagnose/identify the correct conditions based on how they are coded has not been reported for these outcomes.This includes a gap in the literature describing an overall statistic of whether the ICD-10 codes for external cause injuries INPLASY
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".