Anesthetic and Adjunctive Drugs for Fast-Track Surgery
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
With the changes in health care dictated by economic pressure, there has been a realization that hospital stay could be shortened without compromising quality of care. Advances in surgical technology and anesthetic drugs have made an impact in the way perioperative care is delivered with some emphasis on multidisciplinary approach. From the expansion of ambulatory care, lessons were learnt how to apply same concepts to major surgery with the understanding that interventions to attenuate the surgical stress would facilitate the return to "baseline". Beside minimal invasive approach to surgery, anesthesia interventions are arranged with the intent to decrease the negative effects of surgical stress and pain, to minimize the side effects of drugs and at the same time to facilitate the recuperation which follows after surgery. Fast-track or accelerated care encompasses many aspects of anesthesia care, not only preoperative preparation and prehabilitation, but intraoperative attenuation of surgical stress and postoperative rehabilitation. The anesthesiologist is part of this team with the specific mission to use medications and techniques which have the least side effects on organ functions, provide analgesia which in turn facilitates the intake of food and mobilization out of bed. This chapter has been conceived with the intention to direct the clinician towards procedure-specific protocols where the choice of medications and techniques is based on published evidence. The success of implementing fast-track depends more on dynamic harmony amongst the various participants (surgeons, anesthesiologists, nurses, nutrtionists, physiotherapists) than on reaching an optimum level of excellence at each separate organization level.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 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