Cognitive deficit identified via pre-operative patient reported outcome measures is a predictor for post-operative health care utilization in gynecologic oncology patients
Bibliographic record
Abstract
OBJECTIVE: To determine if preoperative Patient-Reported Outcome Measures (PROMs) can be used to predict post-operative health care utilization in gynecology oncology patients. METHODS: A retrospective study was performed after Institutional Review Board approval. PROMs were collected pre-operatively from all patients undergoing surgery for gynecologic malignancy between 1/1/18 and 9/1/19 at a tertiary academic medical center. Patients received the EORTC QLQ-C30 and Patient-Reported Outcomes Measure Information System emotional and instrumental support questionnaires along with a disease specific PROM. Charts were reviewed to ascertain healthcare utilization ("touches") in the 90-day postoperative period defined as phone calls, emails or portal messages, office visits, emergency department (ED) encounters, and hospital admissions. Linear regression was used to identify PROMs associated with postoperative healthcare utilization. RESULTS: Three hundred seventy-one patients were administered questionnaires of which 307 (82.7%) completed PROMs and were included for analysis. A significant association was noted between adverse responses on many EORTC QLQ-C30 domains and increased postoperative healthcare utilization. Of all questions, "difficulty remembering things" was most strongly associated with both overall utilization (total touches p=0.01) and utilization in multiple domains. After forward selection against other significant PROMs, as difficulty with memory increased, both overall healthcare utilization (adjusted regression coefficient 1.9, p=0.01) and ED encounters increased (adjusted regression coefficient 0.10, p=0.01). CONCLUSION: Patients who report cognitive concerns are significantly more likely to have higher healthcare utilization postoperatively. Attention to this preoperatively could help identify patients at higher risk and further studies should determine if additional support around surgery can mitigate this risk.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".