Bibliographic record
Abstract
With this issue’s focus on Health Technology Assessment (HTA), the Italian Journal of Public Health has tackled an area of growing importance in today’s increasingly complex health care delivery systems. As the articles in this issue demonstrate, HTA has grown from a relatively narrow technical focus to a form of policy research underway in dozens of countries. Since its inception just over three decades ago,HTA has evolved through three distinct phases: the machine, the disease and the delivery mode, with the third of these still underway. As the focus has shifted from machines to disease conditions to service delivery approaches, HTA has drawn on research and modes of discourse from a growing variety of disciplines. Thus, despite the evolution that continues, HTA remains, at its core, both multidisciplinary and pragmatic, for the strengths of HTA arise from its integration of the efforts of actors in multiple, diverse disciplines with a view to producing knowledge that will assist decision-makers. The machine phase was marked by a focus on the technical performance of health technologies, often embodying innovative approaches to diagnosis or treatment of human illness. Given the newness and costliness of many technologies selected for assessment, a significant emphasis was placed on assessing the safety of these devices. Imaging technologies were the subject of assessment in many settings, perhaps in part because devices such as the CT scanner produced remarkable visual results that were heralded as affording breakthroughs in diagnosis and treatment. One need only look through the programs of early HTA conferences to see the emphasis on high cost, infrastructure-intensive health technologies that was the hallmark of the machine period.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".