Bibliographic record
Abstract
The reporting of microsurgical outcomes has been variable. Historically, emphasis has been placed on flap and digit survival or failure in the case of free-tissue transfer or digit replantation, respectively. Outcomes have also been measured with indices such as range of motion or grip strength for digital replantations, the ability to eat or talk for head and neck microsurgery, and the ability to walk or return to work for lower extremity microsurgery. Although relevant, this type of reporting of outcomes may fail to capture the effectiveness of microsurgical intervention from the patient's, the third-party payer's, or society's perspective. Significant events have arisen in the past two decades, including the emphasis on outcomes research, recent recommendations to adopt evidence-based microsurgery, and the inclusion in academic training programs of the competency "manager" to the health care system. This necessitates rethinking the way we report outcomes in microsurgery. This article explains the need to (1) use health-related quality-of-life scales to measure the benefits of microsurgical interventions, (2) measure outcomes with high-quality clinical research designs, and (3) incorporate proper cost-effectiveness studies in our clinical research before adopting new technologies such as new free flaps or techniques.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".