Automated patient assessments after outpatient surgery using an interactive voice response system.
Bibliographic record
Abstract
OBJECTIVE: To test the feasibility and utility of an interactive voice response system (IVRS) for monitoring patients after outpatient surgery. METHODS: We studied consecutive patients undergoing gynecologic day surgery. The IVRS called patients on the first postoperative day and asked them if they were experiencing new problems. Feasibility was assessed in terms of call responses and acceptance by patients. Utility was measured in terms of the ability of the IVRS to identify adverse events (AEs), defined as procedure-related symptoms requiring a physician or hospital visit. We contacted patients 30 days later to elicit their perceptions of the IVRS and determine AE status. RESULTS: Follow-up was complete for 249 of 270 enrolled patients (92%). The IVRS successfully contacted 130 patients (52%). Of the 22 patients (17%) who required a follow-up phone call, 9 had a new problem related to surgery, 7 had new or worsening symptoms, 6 wanted to speak with a nurse, and 1 had a medication-related problem. Patients remembering the automated call (n = 96) reported the system easy to use (82%) and comprehend (86%). Most patients (68%) preferred the IVRS to a personal follow-up call (probability greater than 50%, P <.001). AEs occurred in 40 patients (16%; 95% confidence interval = 12%, 21%). The IVRS did not identify any AEs because 90% of these occurred after the automated call. CONCLUSION: An IVRS-based method of monitoring outpatient surgery patients is feasible. To improve utility, calls must occur later than first postoperative day.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".