The impact of a quality management program for patients undergoing head and neck resection with free-flap reconstruction: Longitudinal study examining sustainability
Bibliographic record
Abstract
BACKGROUND: Care pathways (CPs) are helpful in reducing unwanted variation in clinical care. Most studies of CPs show they improve clinical outcomes but there is little known about the long-term impact of CPs as part of a sustained quality management program. Head and neck (HN) surgery with free flap reconstruction is complex, time-consuming and expensive. Complications are common and therefore CPs applied to this patient population are the focus of this paper. In this paper we report outcomes from a 9 year experience designing and using CPs in the management of patients undergoing major head and neck resection with free flap reconstruction. METHODS: The Calgary quality management program and CP design is described the accompanying article. Data from CP managed patients undergoing major HN surgery were prospectively collected and compared to a baseline cohort of patients managed with standard care. Data were retrospectively analyzed and intergroup comparisons were made. RESULTS: Mobilization, decannulation time and hospital length of stay were significantly improved in pathway-managed patients (p = 0.001). Trend analysis showed sustained improvement in key performance indicators including complications. Return to the OR, primarily to assess a compromised flap, is increasing. CONCLUSIONS: Care pathways when deployed as part of an ongoing quality management program are associated with improved clinical outcomes in this complex group of patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".