Software must not manipulate the physicians:” The IT Challenge to Patient Care
Bibliographic record
Abstract
Information technology plays an increasingly important role in the medical working environment. Besides facilitating improvements in the quality of health care, it might also bear some unwished effects. Examining the „m aking? of a diagnosis and the role it plays in m odern m edicine leads to the question how far this pro cess of „diagn osing? m ig ht be affected b y the „tech nical su rroundings?. A num b er of exam ples from clin ical medicine in the hospital and the ambulatory sector illustrate the way IT is being utilised in modern medicine. A tw ofold negative effect could result from this „com p uterisation?: Firstly, the technical requirem ents for the use of IT might force the process of diagnosing to be adapted with subsequent wrong or altered diagnoses. Secondly, constraints like cost control might be facilitated by IT and thus its application might cause the doctors trying to avoid such pressures by modifying the diagnosis and potentially worsening treatment and outcome.
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 | 0.043 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".