CONSISTENCY IN DOCUMENTATION OF TRIGGER EVENTS AND RELATED DIAGNOSES DURING 911 TRANSFERS TO ED NH RESIDENTS.
Bibliographic record
Abstract
Previous research has identified that documentation practices during resident transitions from long-term care (LTC) to the emergency department (ED) can be inconsistent or nonspecific, leading to the receipt of inappropriate or insufficient care. When many older adults (>65 years) in LTC have cognitive impairments that make communication difficult and changes in resident health can present ambiguously, consistent documentation becomes particularly critical for the provision of safe, timely and appropriate care. The purpose of this study was to examine documentation practices across care settings related to reason for transfer during transitions of older adults from LTC to the ED. We tracked every resident transfer from 38 participating LTC facilities to two included EDs in two Western Canadian provinces from July 2011 to July 2012. Using case-related data gathered from 637 transitions, we employed qualitative content analysis to categorize whether documentation practices from LTC to the ED were sufficiently reported and clinically consistent. Transitions were defined as consistent when symptomatology, trigger events and diagnoses aligned in a medically-intuitive manner. Inconsistency patterns were further categorized as minor (indicating one outlying symptom/trigger) and major (indicating more than two inconsistencies). Of the total 637 transitions, 2.67% contained too little data to be accurately categorized. The majority of cases (75.82%) of cases had consistent documentation, 13.19% had minor inconsistencies, and 8.32% had major inconsistencies. These results support that shared continuing education for documentation practices should occur across care settings to ensure that documentation practices are sufficient, support a geriatric focus and consider differing clinician perspectives.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".