Validation of the V66.7 Code for Palliative Care Consultation in a Single Academic Medical Center
Bibliographic record
Abstract
BACKGROUND: Use of administrative data to study the effectiveness of specialized palliative care is limited by the lack of a reliable method to identify patients receiving palliative care consultation. The International Classification of Diseases, Ninth Revision (ICD-9) code V66.7 has been used, but its validity for this purpose is unknown. OBJECTIVE: To examine the validity of the ICD-9 code V66.7 for identifying whether hospitalized patients received palliative care consultation. DESIGN: Retrospective cohort study. SETTING/SUBJECTS: All patients of age ≥18 years admitted to a single academic medical center between August 2013 and August 2015. MEASUREMENTS: Sensitivity and specificity of the V66.7 code for palliative care consultation for all patients and several a priori identified subgroups. The reference standard was the presence of a palliative care consultation note in the electronic medical record. RESULTS: Of 100,910 admissions, 1999 received a palliative care consultation (2.0%) and 1846 (1.8%) had usage of the V66.7 code. Sensitivity and specificity for the V66.7 code were 49.9% and 99.1%, respectively. Sensitivity was considerably higher for certain subgroups, such as patients with dementia (76.3%) and metastatic cancer (66.3%); addition of age restrictions further improved sensitivity while maintaining high specificity. Specificity was substantially lower for patients who died during hospitalization (sensitivity 53.9%, specificity 75.1%). CONCLUSIONS: In a single center, the ICD-9 code V66.7 had poor sensitivity and high specificity for identifying hospitalized patients who received a palliative care consultation. Appropriate use of this code for this purpose should take these characteristics into consideration.
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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 | 0.017 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".