Bibliographic record
Abstract
Mr. B presents to the ED with a 4 day history of dyspnea. He is a smoker, and was diagnosed one year ago with systolic heart failure (NYHA II). He has a history of hypertension, and is on enalapril 10mg PO BID and labetalol 200mg PO q12h. Physical exam reveals bilateral crackles and moderate peripheral edema. The ED physician orders a chest X-ray, and observes signs of pulmonary edema. A decision is made to admit Mr. B, but it proves difficult to diurese him, and the decision is made to insert a Foley catheter on the ward. On the third night of his stay, he complained to a member of the cleaning staff of severe pain in his right leg. The staff member subsequently notified the nurse, who was able to contact the resident on call. A bedside ultrasound was performed, and confirmed the presence of a DVT. The resident also noted that the patient had not been started on DVT prophylaxis. After morning rounds the patient was started on anticoagulation, and his pain resolved within a few hours. Now on his 4th day in hospital, the nurse noted that Mr. B was now febrile, and that he was producing cloudy urine. The catheter is removed and Mr. B is started on empiric antibiotic therapy, and a few days later the infection resolves. However, Mr. B spent 5 extra days in hospital and was discharged feeling extremely displeased with his care. You are the hospital director of quality improvement, and have been asked to review the case and suggest solutions.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".