ICD-10 Diagnostic Coding for Identifying Hospitalizations Related to a Diabetic Foot Ulcer
Bibliographic record
Abstract
PURPOSE: To estimate the positive predictive value (PPV) of Canadian ICD-10 diagnostic coding for the identification of hospitalization related to a diabetic foot ulcer (DFU). METHODS: Hospitalizations related to a neuropathic and/or ischemic DFU were identified from the Discharge Abstract Database (DAD) records of a single Canadian tertiary care hospital between April 1, 2002 and March 31, 2019. The first coding approach required a most responsible diagnosis (MRDx) code for diabetes-specific foot ulceration or gangrene (DSFUG group). Three alternative coding approaches were also considered: MRDx code for lower-limb osteomyelitis (osteomyelitis group); lower-limb ulceration (LLU group); or lower-limb atherosclerotic gangrene (atherosclerosis group)-each in conjunction with a non-MRDx DSFUG code on the same DAD record. From all eligible DAD records, random samples were drawn for each coding group. DAD records were independently compared by a masked reviewer who manually abstracted data from the entire hospital record (reference standard). The PPV and 95% CI were generated. RESULTS: Out of 1,460 hospitalizations, a total of 300, 50, 33 and seven records were included from the DSFUG, osteomyelitis, LLU and atherosclerosis samples, respectively. Compared to the reference standard, the PPV for all 390 records was 88.5% (95% CI 84.9 to 91.5). The DSFUG group had the highest PPV (90.0%, 95% CI 86.0 to 93.2), followed by the atherosclerosis (85.7%, 95% CI 42.1 to 99.6), LLU (84.9%, 95% CI 68.1 to 94.9) and osteomyelitis (82.0%, 95% CI 68.6 to 91.4) groups. CONCLUSION: Based on data from a Canadian tertiary care hospital, the specified coding algorithms can be used to identify and study the management and outcomes of people hospitalized with a DFU in Ontario.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".