The relationship between patient activation and surgical outcomes: A pilot study
Bibliographic record
Abstract
Background:Patient activation is a behavioral concept, defined as a patient's knowledge, skills, beliefs and confidence to manage their own health care.In patients with chronic medical conditions, there is a strong association between higher levels of activation and improved healthcare outcomes, higher patient satisfaction, lower rates of health care system utilization and lower costs.However, there is very little evidence investigating the role of patient activation in surgical patients.The purpose of this study was to estimate the extent to which low preoperative activation predicts emergency department (ED) visits, complications, adherence with perioperative care processes and satisfaction after colorectal surgery. Methods:A secondary analysis of data obtained from a randomized trial performed in 2017 at the McGill University Health Center assessing the impact of a mobile health application on adherence with care processes (clinicaltrials.govidentifier NCT03277053) was performed.Participants were adult patients with colonic or rectal diseases who underwent colorectal surgery.The main exposure was patient activation, measured using the Patient Activation Measure (PAM) survey at baseline and before hospital discharge, and classified as high or low.The main outcome was ED visits within 30 days of surgery after hospital discharge.Secondary outcomes were complications, patient satisfaction and adherence to a postoperative colorectal surgery care pathway.Distribution of characteristics was compared between patients with high and low activation using Chi-square or Fisher's exact test and ttest or ANOVA for categorical and continuous variables, respectively.A univariate logistic regression was performed to determine predictors of return to the ED and complications.A multivariate logistic regression including complications, age, gender, comorbidity index and diagnosis was used to estimate the effect of low preoperative activation on return to the ED.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".