Metabolic acidosis is undertreated and underdiagnosed: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Guidelines recommend treatment of metabolic acidosis (MA) in patients with chronic kidney disease (CKD), but the diagnosis and treatment rates in real-world settings are unknown. We investigated the frequency of MA treatment and diagnosis in patients with CKD. METHODS: In this retrospective cohort study, we examined administrative health data from two US databases [Optum's de-identified Integrated Claims + Clinical Electronic Health Record Database (US EMR cohort; 1 January 2007 to 30 June 2019) and Symphony Health Solutions IDV® (US claims cohort; 1 May 2016 to 30 April 2019)] and population-level databases from Manitoba, Canada (1 April 2006 to 31 March 2018). Patients who met laboratory criteria indicative of CKD and chronic MA were included: two consecutive estimated glomerular filtration results <60 mL/min/1.73 m2 and two serum bicarbonate results 12 to <22 mEq/L over 28-365 days. Outcomes included treatment of MA (defined as a prescription for oral sodium bicarbonate) and a diagnosis of MA (defined using administrative records). Outcomes were assessed over a 3-year period (1 year pre-index, 2 years post-index). RESULTS: A total of 96 184 patients were included: US EMR, 6179; Manitoba, 3223; US Claims, 86 782. Sodium bicarbonate treatment was prescribed for 17.6%, 8.7% and 15.3% of patients, and a diagnosis was found for 44.7%, 20.9% and 20.9% of patients, for the US EMR, Manitoba and US Claims cohorts, respectively. CONCLUSIONS: This analysis of 96 184 patients with laboratory-confirmed MA from three independent cohorts of patients with CKD and MA highlights an important diagnosis and treatment gap for this disease-modifying complication.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".