Bibliographic record
Abstract
Currently no mandatory standards or guidelines exist for Point-of-Care Testing (PoCT) in Australia. In 2001, a report on the role and value of 'near patient testing' in general practice outlined work that was required to assist the Australian Government to decide how to manage PoCT. Phillips Fox reported that adoption of mandatory accreditation requirements was not justified by the level of risk associated with PoCT. If implemented appropriately, PoCT could be useful with frontline management of chronic disease, relieving stress on general practice and expanding the reach of pathology. Interim PoCT standards in general practice were developed by a Quality Use of Pathology committee, and formed an accreditation framework for the PoCT in General Practice Trial. This trial concluded that PoCT has a role in supporting the primary healthcare team to manage chronic disease patients. While results of the trial are still being considered, the potential impact of funding PoCT in general practice is being treated as part of the wider review of pathology funding currently taking place in Australia. Although Australia has local models from which to draw experience, it has yet to decide the quality framework it would adopt if it was to roll out PoCT in general practice. The quality framework that Australia adopts for PoCT must achieve high quality pathology results that enhance clinical care.
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 | 0.033 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".