Patient perspectives on open vs. minimally invasive thoracic surgery (PPOMITS): survey and experience from a single academic institution
Bibliographic record
Abstract
Background: Despite the widespread acceptance of safety and oncologic equivalence of minimally invasive thoracic surgery, adoption by thoracic surgeons is lagging. Patient perspectives on minimally invasive thoracic surgery versus open surgical approaches has not been well studied. The aim of this survey was to document patient perspective on pain, complication risks, cosmesis, travel burden, and functional outcomes and their relationship to surgical approach. Methods: From 2012–2017, 201 thoracic surgical patients were prospectively enrolled in this observational cohort study. Participants completed a RAND36 short form health survey and a PPOMITS (patient perspectives on open vs. minimally invasive thoracic surgery) questionnaire. Variables of interest were measured on a continuous visual analog scale. PPOMITS questions were classified into three anatomic regions (neck, chest, and abdomen). Surveys were completed preoperatively, then at 1 and 6 months postoperatively. Chi-squared, Fisher’s, and independent t-test were used as appropriate. Results: A total of 201 patients were surveyed. Recovery of indices was similar in both minimally invasive surgery (MIS) and open surgery patients. On average, patients placed greater importance on postoperative pain (6.93; 95% CI: 6.69–7.17) than incision size (4.31; 95% CI: 4.0–4.63, P<0.001) and travel burden (4.35; 95% CI: 4.04–4.66, P<0.001). Risk of complications (7.36; 95% CI: 7.14–7.58) was also given more importance than incision size (P<0.001) and travel burden (P<0.001). Findings were similar at each time point and across body regions. Importance of postoperative pain was similar between both groups regardless of surgical site and timing. RAND SF-36 results indicated a significant decline in physical functioning, role limitations due to physical health, energy level, pain, and social functioning at 1 month. All indices recovered to baseline at 6 months. Conclusions: Early deterioration with recovery of functional outcomes at 6 months were similar regardless of surgical approach. Risk of complications was more important to patients than incision size, pain, and distance traveled for treatment. Our results suggest that patients may be willing to enter randomized trials comparing minimally invasive and open approaches, in regionalized cancer care models.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".