New ICD-11 features for coding late sequelae and chronic post-procedural conditions
Bibliographic record
Abstract
There are many clinical circumstances in life where people live with chronic conditions (or states) that arose from either (1) a prior clinical diagnosis (e.g. a stroke) or (2) a prior healthcare-related event or medical procedure. Unfortunately, capturing such concepts is not straightforward in coded health data. This paper describes the coding rubric for sequelae (also often referred to as 'late effects') in the new ICD-11 coding system and some clinical coding examples. Earlier versions of ICD were constrained, in all but a few exceptions, by the need to combine all aspects of a clinical scenario into a single code. ICD-11 permits the clustering (postcoordination) of multiple codes to describe multifaceted clinical scenarios. This article features both precoordinated (single code) and postcoordinated (multi-code) descriptions of late effect situations where a prior health problem is the remote cause of current symptoms or conditions - i.e. sequelae. The late effect of a prior health problem rubric is yet another example of enhanced ICD-11 features that will improve future health information systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".