Algorithm based patient care protocol to optimize patient care and inpatient stay in head and neck free flap patients
Bibliographic record
Abstract
OBJECTIVE: To determine if rigid adherence (where medically appropriate) to an algorithm/checklist-based patient care pathway can reduce the duration of hospitalization and complication rates in patients undergoing head and neck reconstruction with free tissue transfer. METHODS: Study design was a retrospective case-control study of patients undergoing major head and neck cancer resections and reconstruction at a tertiary referral centre. The intervention was rigid adherence to a pre-existing care pathway including flow algorithms and multidisciplinary checklists incorporated into patient charting and care orders. 157 patients were enrolled prospectively and were compared to 99 patients in a historical cohort. Patient charts were reviewed and information related to the patient, procedure, and post-operative course was extracted. The two groups were compared for number of major and minor complications (using the Clavien-Dindo system) and length of stay in hospital. RESULTS: Comparing pre- and post-intervention groups, no significant difference was identified in duration of hospital stay (21.5 days vs. 20.5 days, p = 0.750), the rate of major complications was significantly higher in the pre-intervention cohort (25.3% vs. 14.0%, p = 0.031), the rate of minor complications was not significantly higher (34.3% vs 30.8%, p = 0.610). CONCLUSION: Rigid adherence to our patient care pathway, and improved charting techniques including flow algorithms and multidisciplinary checklists has improved patient care by showing a significant reduction in the rate of major complications.
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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 | 0.013 | 0.025 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".