Artificial intelligence for medical writing in doctors of Punjab; a cross-sectional study
Bibliographic record
Abstract
Objectives: To determine the frequency and perceptions regarding the use of artificial inte ligence in medical writing among doctors. METHODS: The analytical, cross-sectional study was conducted from July to September 2023 in Punjab, Pakistan, after approval from the ethics review board of Akhtar Saeed Medical and Dental College, Lahore and comprised serving doctors with at least two publications during the study period. Data was collected using a questionnaire that explored sociodemographic details, frequency of artificial intelligence usage, and the subjects' perceptions regarding its use in medical writing. Data was analysed using SPSS 23. RESULTS: Of the 445 respondents, 248(55.7%) were females and 197(44.3%) were males. The overal mean age was 38.7±10.91 years, and the number of mean publications was 6.82±10.2. Awareness level regarding use of artificial inte ligence in medical writing was high 413(92.8%), with 110(24.7%) having already used it, and 368(82.7%) considering its use in the future. Concerns about confidentiality breaches was the most common barrier to using artificial intelligence 307(69%). CONCLUSIONS: About a quarter of the sample studied had used artificial intelligence for medical writing.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | MetaresearchScholarly communication Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".