Are stone analysis results different with repeated sampling?
Bibliographic record
Abstract
INTRODUCTION: We assessed differences in results of stone analyses on subsequent sampling. METHODS: A retrospective review of patients with stone analyses at a tertiary stone centre between March 2006 and July 2012 was performed. All stones were analyzed at a centralized laboratory using infrared spectroscopy. Patients were grouped according to the first predominant stone type on record, as defined by the predominant stone component of at least 60%. Stone groups included calcium oxalate (CaOx), calcium phosphate (CaP), uric acid (UA), cystine, struvite, mixed CaOx-CaP and mixed CaOx-UA. All patients had a full metabolic stone workup. RESULTS: Of the 303 patients with stone analyses, 118 (38.9%) patients had multiple stone analyses. The mean age was 53.4 ± 15.1 years, and 87 (73.7%) were males. Of the 118, the initial stone analysis showed 43 CaOx, 38 CaP, 21 UA, 4 CaOx-CaP, 2 CaOx-UA, 6 cystine, and 4 struvite. There was a different stone composition in 25 (21.2%) patients with a median time delay of 64.5 days. Different compositions were found in 7 CaOx (to 3 CaP, 2 CaOx-CaP, and 2 UA), 5 CaP (to 3 CaOx and 2 CaOx-CaP), 3 UA (to 3 CaOx), 4 CaOx-CaP (to 2CaOx, 1 UA and 1 CaP), 2 CaOx-UA (to 2 CaOx) and 4 struvite (to 3 CaP and 1 UA). CONCLUSIONS: Stone composition was different in 21.2% of patients on subsequent analyses.
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 | 0.062 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".