Bibliographic record
Abstract
One Good NoteRecently, one of our staff cardiologists thanked me for my note on a patient who was transferred from the coronary care unit, where I practise, to the cardiology ward, where she was on service.What impressed me even more than her actually knowing my name (we don't usually work together) was her acknowledgement of my documentation.Her follow-up comment was that my note was very legible-I had to infer that the content of the note was just as helpful as its legibility.This was not the first time I'd received such feedback, and it's the same theme every time: the note was a good one, with primary emphasis on its legibility.Because I work in a critical care area and as such am often involved when patients are transferred to other units, I try to write at least "one good note" for each of my patients, typing the note on a computer, printing it out, and then signing it before adding it to the chart (my institution still uses paper-based charts).I use a blank word-processing file and employ a modified DAP (data, assessment, plan) format that includes a brief history, medication reconciliation, and, most important, a treatment plan.The feedback I've received regarding my notes has led me to contemplate the nature of documentation in pharmacy practice and health care in general.Why is it that the single most important perceived advantage of my notes is their legibility?How can something so simple be so valuable?We all know the stigma of physicians' handwriting.Despite our reputation for being able to decipher poor handwriting, as
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 | 0.003 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.066 | 0.027 |
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".