A Diagnostic Electronic Reporting Framework Proposal Using Preassigned Automated Coded Phrases
Bibliographic record
Abstract
Radiologists daily diagnose a large number of Chest X-rays and it is crucial that these reports are appropriately recorded, meaningfully indexed, carefully stored, easily retrieved, shared and printed. The absence of organized reports' storage does not permit their direct and easy retrieval, while after almost a year the report is perished and not even readable (handwritten or typed). The scope of this paper is to evaluate and propose the use of preassigned automated-coded phrases for the chest X-ray electronic reporting in a Radiology Department. The research included 9,252 typed reports, using the proposed method and 949 hand written reports (later typed or not), which were used to compare between the time being spent in reporting with either method. The results proved that even if the method could not be applied fully, there was a 90% reduction of the time being spent by the radiologists and secretarial staff in a Radiology Department, thereby facilitating the typing and management of the electronic archives. In addition, it was found that the reprinting due to addendums/discrepancies, when the proposed method was used, was reduced fourfold, when compared to the previously used methods. In conclusion, the consistent application of preassigned automated-coded reporting can be time saving, cost effective and environmentally friendly saving paper and ink.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".