Bibliographic record
Abstract
There are many well known benefits about electronic charting: easy access and the ability to share the context, readability of MD writing, standardization of documentation and care plans, real time documentation and authentication and many other more or less important things.First, I want to start the discussion and see if somebody else is interested in this specific opportunity technology presents to us.I call it: dynamic representation of abnormalities.You see, when nurses on the medical / surgical floors change their shifts they pass very specific, objective, standard, and dynamic clinical information about the progress of their clients, like the presence and the intensity (rate) of bleeding, presence of peristalsis sounds, quality of the abdominal wall (soft / hard), VS, input and output and etc.This dynamic data allows for the quick capture of important information that allows for fast clinical judgment about the client's condition.They usually do not pass any information that has no relevancy to the particular procedure or diagnosis.It takes about 15 min to go through 20 -25 medical surgical clients in this fashion.Also, nurses usually play a very technical and task oriented role.There are certain tasks to perform in terms of care, but there is no need to read surgical reports or chemotherapy plans or others (I know that many nurses read all these, but you know that this is not a main stream).MD is in charge of the overall care and usually needs to know only abnormal dynamic clinical data, meaning: when things are not according to the established norms and standards for some particular procedure / surgery / treatment.For example: when the temperature, level of pain, WBC or else is higher than it should be for that particular day of treatment.For each surgery or procedure, to my knowledge, there are about 5 to 15 such parameters.When MD is satisfied that the acute phase of care is done and all these parameters show the dynamic consistency with a recovery pass, MD will discharge the client home for GP care.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".