The Development of ICD Adaptations and Modifications as Background to a Potential Saudi Arabia's National Version
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
Modified national versions of the WHO’s International Statistical Classification of Diseases, current version ICD-10 with ICD-11 coming into effect in January 2022, have become the standard in many countries for diagnosis and procedure coding to facilitate the submission of medical billing and reimbursement by health insurers. The WHO ICD-10 exists purely as a coded classification of disease. It has no related classification of procedures and lacks the clinical level of diagnostic specificity necessary for the documentation of individual clinical cases and the associated prescribed therapies and interventions, particularly surgical cases. Historically, the US clinical modification of ICD-9, known as ICD-9-CM, established the trend. Australia adopted ICD-9-CM, later adapted it to Australian clinical specifications, and after the launch of the WHO ICD-10 produced the current Australian modification ICD-10-AM, used under license by many other countries. This paper examines a work in progress, rather than offering an academic critique, to illustrate the evolution of national clinical modications with particular reference to those of the United States, Australia and Thailand. The selection is based on the historical ICD-9-CM connection of the US and Australia, and the fact that Thailand is a more advanced developing nation like Saudi Arabia. The study parameters include the Saudi national healthcare system which has not previously employed a classification clinical coding, despite the wealthy developing healthcare system. Nations using their own modification face the burden of upgrading. Saudi Arabia plans to implement the national Australian modification, rather than creating a Saudi national modification.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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