Frequency and clinical relevance of inconsistent code status documentation
Bibliographic record
Abstract
BACKGROUND: Accurate and complete documentation of hospitalized patients' code status is important to ensure that healthcare providers take appropriate action in the event of a cardiac arrest. OBJECTIVE: Determine the frequency and clinical relevance of incomplete and inconsistent code status documentation. DESIGN: Point-prevalence study. SETTING: Academic medical centers. PATIENTS: Patients admitted to general internal medicine wards. MEASUREMENTS: Frequency and clinical relevance of inconsistent code status documentation across 5 documentation sources. RESULTS: Thirty-eight (20%; 95% confidence interval [CI], 14%-26%) of 187 patients had complete and consistent code status documentation. Another 27 (14%; 95% CI, 9%-19%) patients had no code status documentation. The remaining 122 (65%; 95% CI, 58%-72%) patients had at least 1 code status documentation inconsistency. Of these, 38 (20%; 95% CI, 14%-26%) patients had a clinically relevant code status documentation inconsistency. Multivariate logistic regression analysis demonstrated that increased age (odds ratio [OR] = 1.07 [95% CI, 1.05-1.10] for every 1-year increase in age, P < 0.001) and patients receiving comfort measures (OR = 9.39 [95% CI, 1.35-65.19], P = 0.02) were independently associated with a clinically relevant code status documentation inconsistency. CONCLUSIONS: Incomplete and inconsistent documentation of code status occurred frequently in hospitalized patients, especially elderly patients and patients receiving comfort measures. Having multiple, poorly integrated code status documentation sources leads to a significant number of concerning inconsistencies that create opportunities for healthcare providers to inappropriately deliver or withhold resuscitative measures that conflict with patients' expressed wishes. Institutions need to be aware of this potential documentation hazard and take steps to minimize code status documentation inconsistencies.
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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 | 0.006 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".