Enhanced recovery pathways in thoracic surgery: the Quebec experience
Bibliographic record
Abstract
BACKGROUND: Canada has a universal public health system where all resources must be allocated to optimize cost-effectiveness. Rapid diagnostic assessment programs (DAPs) and enhanced recovery protocols (ERPs) may improve timeliness of care and postoperative outcomes and thus reduce costs. The use of DAPs and ERPs in lung cancer patients who undergo lobectomy via video-assisted thoracoscopic surgery (VATS) is still controversial. This study measured the time between preoperative workup and treatment with a DAP and evaluated the impact of an ERP postoperatively in patients with early-stage non-small cell lung cancer (NSCLC) who received a VATS lobectomy. METHODS: We conducted a retrospective review of patients who underwent minimally invasive lobectomy for the primary treatment of lung cancer from January 2014 through May 2017 at our institution. Timelines of care were measured. Postoperatively, the duration of chest tube drainage, length of hospital stay, and incidence of complications were noted. RESULTS: During the study period, 646 patients underwent VATS lobectomy for stage I or II NSCLC; of these, 384 (59%) were assessed within the DAP. Using the DAP, the median time between the patient's first clinic visit and referral to surgery was 30.0 days [interquartile range (IQR), 21.0-40.0 days), and the median time between surgical consult and treatment was 29.0 days (IQR, 15.0-47.5 days). With the ERP, the median duration of chest drainage was 3.0 days (IQR, 2.0-6.0 days), and median hospital stay was 4.0 days (IQR, 3.0-7.0 days). CONCLUSIONS: DAPs and ERPs have promising roles in thoracic surgical practice. A rapid DAP can expedite the care trajectory of patients with lung cancer and has allowed our institution to adhere to governmental standards for the management of lung cancer. ERPs are feasible to establish and can effectively improve clinical outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".