Dissemination of discharge summaries. Not reaching follow-up physicians.
Bibliographic record
Abstract
OBJECTIVE: To discover how often hospital discharge summaries were available to physicians seeing patients for follow-up visits after hospitalization. DESIGN: Cohort study. SETTING: Teaching hospital in Ottawa, Ont. PARTICIPANTS: We studied 792 patients discharged from an internal medicine service after treatment for acute illness. We determined when and by which physician each patient was seen during the first 6 months after discharge. We also determined the date each patient's discharge summary was printed and the physicians to whom it was sent. We confirmed that summaries were received by means of a survey or by telephoning physicians' offices. Patients were observed for 6 months or until they were readmitted to hospital. MAIN OUTCOME MEASURES: Proportion of follow-up visits to physicians for which discharge summaries were available. RESULTS: During the observation period, patients made 6619 visits (median six per patient, interquartile range [IQR] 2 to 9) to 914 different physicians (median three per patient, IQR 2 to 4). Discharge summaries were available for only 996 (15%) visits. Summaries were available for only 65 initial visits (8.2%); no summaries were available for any visit for 542 (68.4%) patients. Summaries were most commonly unavailable because they were not generated in time for follow-up visits (20.0%) or were not sent to follow-up physicians (50.8%). CONCLUSION: At our institution, discharge summaries often did not get to physicians seeing patients after discharge from hospital.
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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 | 0.010 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".