Validating Diagnostic Information on the Minimum Data Set in Ontario Hospital-Based Long-Term Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Over 20 countries currently use the Minimum Data Set Resident Assessment Instrument (MDS) in long-term care settings for care planning, policy, and research purposes. A full assessment of the quality of the diagnostic information recorded on the MDS is lacking. OBJECTIVE: The primary goal of this study was to examine the quality of diagnostic coding on the MDS. STUDY SAMPLE: Subjects for this study were admitted to Ontario Complex Continuing Care Hospitals (CCC) directly from acute hospitals between April 1, 1997 and March 31, 2005 (n = 80,664). METHODS: Encrypted unique identifiers, common across acute and CCC administrative databases, were used to link administrative records for patients in the sample. After linkage, each resident had 2 sources of diagnostic information: the acute discharge abstract database and the MDS. Using the discharge abstract database as the reference standard, we calculated the sensitivity for each of 43 MDS diagnoses. RESULTS: Compared with primary diagnoses coded in acute care abstracts, 12 of 43 MDS diagnoses attained a sensitivity of at least 0.80, including 7 of the 10 diagnoses with the highest prevalence as an acute care primary diagnosis before CCC admission. CONCLUSIONS: Although the sensitivity was high for many of the most prevalent conditions, important diagnostic information is missed increasing the potential for suboptimal clinical care. Emphasis needs to be put on improving information flow across care settings during patient transitions. Researchers should exercise caution when using MDS diagnoses to identify patient populations, particularly those shown to have low sensitivity in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it