Use of local data to enhance uptake of published recommendations: an example from the diagnostic evaluation of precocious puberty
Bibliographic record
Abstract
BACKGROUND: It has been recommended that basal luteinising hormone (LH) levels be used as the initial test to identify cases of central precocious puberty (CPP) in children. However, in clinical practice, gonadotropin-releasing hormone (GnRH) stimulation tests are frequently still used. OBJECTIVE: To assess the diagnostic utility of a single LH to identify CPP in girls, as a means to safely reduce GnRH stimulation testing rates. DESIGN: Retrospective analysis of patients referred for GnRH stimulation between August 2007 and December 2010, with prospective 12-month follow-up of GnRH stimulation testing rates post implementation of management algorithm. PATIENTS: 57 girls (6.2 ± 2.1 years) with early signs of puberty. MAIN OUTCOME MEASURE: Ability of basal LH to predict clinical pubertal progression, 6 months following the GnRH stimulation test. RESULTS: Pubertal progression occurred in 18 patients. All patients with a basal LH level ≥ 0.3 IU/L had subsequent pubertal progression, while 39 of 41 patients with a basal LH ≤ 0.2 IU/L did not progress, resulting in 100% specificity (95% CI 92% to 100%) and 90.5% sensitivity (69.6% to 98.8%). Using the locally derived algorithm, GnRH stimulation testing was redirected to patients with pubertal progression that was discordant with basal LH data. Post intervention, there was a 75% reduction in GnRH stimulation testing without comprising the rate of diagnosis of CPP. CONCLUSIONS: Our results confirm the diagnostic utility of basal LH levels in the diagnosis of CPP and demonstrate that dissemination and interpretation of local data may facilitate change in clinical practice, resulting in streamlined patient care and cost savings.
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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 | 0.218 | 0.424 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".