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Record W2795752023 · doi:10.1111/hdi.12658

Metastatic pulmonary calcification: Experience from a single center in Singapore

2018· article· en· W2795752023 on OpenAlexvenueno aff
Swee Ping Teh, Yuen Li Ng, Anthony Yii, Hui Lin Choong, Jiunn Wong

Bibliographic record

VenueHemodialysis International · 2018
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging and Pathology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineParathyroidectomyAsymptomaticRadiological weaponCalcificationSingle CenterHemodialysisElevated alkaline phosphataseSurgeryRadiologyInternal medicineGastroenterologyParathyroid hormoneUrologyAlkaline phosphataseCalcium

Abstract

fetched live from OpenAlex

Metastatic pulmonary calcification (MPC) was seen in 79% of patients with end-stage renal disease (ESRD) during autopsy. However, it is not commonly diagnosed in vivo. Its pathogenesis is not fully understood. We report a retrospective series of 5 cases of MPC from a single center in Singapore. MPC were diagnosed using radiological or histological features. Mean onset of MPC from diagnosis of ESRD was 22.6 ± 3.1 years. One patient remains asymptomatic. Four patients died, one was related to MPC. All patients had calcifications at the lung apices on radiological studies. Three patients with MPC were diagnosed based on radiological features while 2 had histological features. Four patients underwent parathyroidectomy without radiological changes before parathyroidectomy. Median intact parathyroid hormone of this series was 5.6 pmol/L (IQR 1.3-139.4), alkaline phosphatase 74 U/L (IQR 62-461), calcium 2.10 mmol/L (IQR 1.85-2.40), and phosphate 1.30 mmol/L (IQR 0.87-1.63). The observed low iPTH suggests that MPC might occur in low iPTH. Our case series showed MPC might occur in low iPTH after parathyroidectomy, in contrast to existing literature that suggests MPC is diagnosed in patients with elevated iPTH. Parathyroidectomy does not prevent MPC.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.065
GPT teacher head0.345
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2018
Admission routes1
Has abstractyes

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