Follow up of hypertension by family practitioners: Authors' reply
Bibliographic record
Abstract
Random drug testing in schools fails screening criteriaEditor-Last month the prime minister, Tony Blair, lent his weight to random drug testing in schools in an interview for a downmarket newspaper. 1He proposed a national programme be implemented soon, adhering to unspecified central directives.The Department of Health has 19 criteria for introducing new screening programmes. 2 At least 18 of these 19 criteria are not met for widespread, wide spectrum drug urine analysis in schools.The remaining criterion is that the condition is an important health problem.Drug use in young people is indeed associated with many health risks, 3 but a single, positive urine test, for any illicit drug, is probably not meaningful in a clinical sense.Each schoolchild's context of use (family history, social and emotional development) is crucial to interpreting any supposed "drug career."Use by a homeless pregnant teenage runaway from local authority care with a history of deliberate self harm and high risk sex work to pay for her drugs may be very different from a single experimental use at home with adults during a family party.Three failed criteria are especially pertinent to screening for school age drug use:(1) There should be an agreed policy on the further diagnostic investigation of people with a positive test result and on the choices available to them.(2) There should be an effective treatment or intervention for patients identified through early detection.(3) Clinical management of the condition and patient outcomes should be optimised by all healthcare providers before participation in a screening programme.In three years of experience of school health provision for alcohol and drug problems and their related referral networks I do not know of one school that could satisfy these criteria, especially the underpinning policy of promoting informed choice for children and families. 2
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.032 | 0.028 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".