Algorithm based patient care protocol to optimize patient care and inpatient stay in head and neck free flap patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it