Bibliographic record
Abstract
background Severe sepsis, a systemic inflammatory response characterized by acute organ dysfunction in the face of an infectious process, is a major healthcare issue worldwide. Sepsis presents a unique challenge in the management of patients who require anesthesia and definitive surgical treatment. Anesthesiologists are involved in the multidisciplinary management of patients with severe sepsis throughout the patient’s clinical course. 1 The mainstays of management include prompt administration of IV antibiotics in conjunction with preoperative fluid and pharmacological resuscitation. The goal is to optimize end organ perfusion through the judicious use of pressors, inotropes and IV fluids. 2 An example might include a patient with an acute small bowel obstruction complicated by sepsis. The patient would be appropriately resuscitated using a multi-modal approach as outlined above, while definitive management would include surgical decompression of the obstruction. Intra-operatively, these patients require conscientious induction of anesthesia, ample fluid resuscitation coupled with invasive hemodynamic/biochemical monitoring. 1 Efforts to promote a favourable surgical outcome of a septic patient begins before the surgeon picks up the scalpel.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".