Characteristics of Inpatient Falls not Reported in an Incident Reporting System
Bibliographic record
Abstract
An incident reporting system is the most commonly used method to identify patient safety incidents in a hospital. However, non-reporting of incidents for various reasons is a serious problem. We studied the rate of inpatient falls that were not reported in an incident reporting system but were recorded in medical charts and we evaluated characteristics of those falls by comparing with the falls reported in incident reports in a Japanese acute care hospital setting. Falls recorded in medical charts were detected by using a text mining method followed by a manual chart review. About 25% of the recorded falls were not reported in incident reports. Male patients, first fall, long lag time until recording, no witness at the time of the fall and physician profession were shown to be significant factors associated with non-reporting. Our results show that the rate of non-reporting of inpatient falls in a Japanese acute care hospital is compable to that shown in previous studies in other conutries and that the same barriers to incident reporting as those found in previous studies exist in the medical staff.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".