Bibliographic record
Abstract
Limitations of the Summary Care RecordI am glad the clinical leaders of Connecting for Health 1,2 have had a chance to reply to the concerns about data security and confidentiality laid out by Professor Anderson 3 and Gordon Baird 4 in February BJGP.In doing so they showed the weakness of their case and the strength of their opponents.In particular they protested about, 'a number of factual errors and wrongly conflated aspects of the National Programme for IT'.Sadly they failed to show what Professor Anderson's errors actually were.I have no trust in the seemingly limited Summary Care Record.I suspect in future it will become more extensive, and more available, and for purposes beyond direct patient care.It is a part of the expensive and increasingly discredited and distrusted National Programme for IT.It is a thin end of a wedge.The key phrase in Mark Davies et al's editorial is 'Information governance'.The current evidence we have is that the government has no understanding of this, and only limited systems in place to fully secure data against loss.The recent loss of 15 million child benefit records showed this.Equally worrying was the apparent lack of concern among ministers, and the willingness of senior managers to blame the debacle on a junior staff member.My own medical notes have 93c3 'refuses consent to have health records transferred to central database' added to them.I will encourage my patients to do likewise.I think that this will give them more control over their medical records than any centralised system.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.025 |
| Insufficient payload (model declined to judge) | 0.073 | 0.035 |
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".