The Medical Orders for Scope of Treatment (MOST) form completion: a retrospective study
Bibliographic record
Abstract
BACKGROUND: Advance care planning (ACP) involves discussions about patient and families' wishes and preferences for future healthcare respecting autonomy, improving quality of care, and reducing overtreatment. The Medical Orders for Scope of Treatment (MOST) form records person preferred level and types of treatment and intervention. PURPOSE: To examine the MOST form use in inpatient units within a British Columbia (Canada) hospital, estimate and compare its completion rate, and inform health policies for continuous, quality and individualized patient care. METHODS: About 5,000 patients admitted to the participating tertiary acute care hospital during October 2020. Data from 780 eligible participants in medical, surgical, or psychiatry unit were analyzed with descriptive statistics, the chi-square test for group comparisons, and logistic regression to assess predictors of the MOST form completion. RESULTS: = 79.53, p < .001, φ = .319]. Multivariate logistic regression analysis demonstrated that age (OR = 1.05, 95% CI 1.04 to 1.06) and unit admission (OR = .60, 95% CI 0.36 to 0.99 in psychiatry; and OR = .21, 95% CI 0.14 to 0.31 in surgery) were independently associated with the MOST form completion. CONCLUSION: Our findings demonstrate a need for consistent and broad completion of the MOST form across all jurisdictions using, desirably, advanced electronic systems. Healthcare providers need to raise awareness of the MOST completion benefits and be prepared to discuss topics relevant to end-of-life. Further research is required on the MOST form completion.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".