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Record W1943589501 · doi:10.1210/jc.2015-2930

Letter to the Editor: Superior Mass Spectrometry-Based Estrogen Assays Should Replace Immunoassays

2015· letter· en· W1943589501 on OpenAlexaff
Fernand Labrie, Yuyong Ke, Renaud Gonthier, Alain Bélanger

Bibliographic record

VenueThe Journal of Clinical Endocrinology & Metabolism · 2015
Typeletter
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsUniversité LavalTheratechnologies (Canada)
Fundersnot available
KeywordsMass spectrometryEstrogenChromatographyComputer scienceChemistryMedicineInternal medicine

Abstract

fetched live from OpenAlex

Please find what we strongly believe is particularly important for accurate estrogen assays: A conclusion in the statement recently published following the Pooks Hill Workshop (1) that is particularly difficult to understand is: “Both immunoassays and mass spectrometry (MS)-based assays for estrogens and their metabolites would be acceptable if they are accurate and reliable and meet performance criteria suitable for their intended use.” The problem is that nobody can tell when immuno-based assays are “accurate and reliable.” This is due to issues of specificity, selectivity, precision, and reproducibility of the immunoassays (2, 3). In fact, it has been clear for quite some time that the immunoassays should be replaced by validated MS-based assays (4). Immunoassays are a technology of the 1970s that has been very useful. However, two main biases can never be quantitated: 1) lack of specificity is impossible to control in immunoassays; and 2) matrix effects are also not controllable in immunoassays. Moreover, the standard should not be an agreed upon or some “gold” standard (imprecise, often incorrect) but an absolute/true standard. The same difficulty applies to the continuation of the same phrase which reads: “… and meets performance criteria suitable for their intended use.” It is very important to make available the range of normal values for estrogens, but this information has been available for quite some time using MS-based assays. The harmonization that consists in having different laboratories obtaining the same value is not a guarantee of accuracy because all the laboratories involved could well have wrong values. It is true that not every MS-based assay provides valid accurate data if not properly validated and/or not well controlled during assays (5). The standard used in MS-based assays must be an absolute/true reference with certificate of analysis including HPLC profile, HPLC assay, Nuclear Magnetic Resonance, Infrared spectroscopy and residual solvents. Laboratories should have the means and obligation to determine the purity/trueness of the standards used. It is also true that MS-based assays are more expensive and more technically demanding, but this is what is required to obtain accurate data. In fact, all research is costly, and the total expenses of any study are a complete loss if the data are not valid and accurate. “A long-term goal of requiring accurate assays” is not an appropriate statement because accurate assays should be an immediate and not a long-term goal. Please consider how many clinical trials including large epidemiological studies have been performed at high costs but were using unreliable steroid data (6). These clinical trials would need to be repeated due to the problems of the immuno-based assays used to “save costs”?? The accuracy item of the summary (1) would benefit from being focused on the true 2015 situation, not on costs, availability, technical difficulties, etc. Most importantly, if the estrogen assays are done using a validated MS-based technology, there will be no significant differences in the data obtained in different laboratories. Disclosure Summary: F.L. (CEO of EndoCeutics), Y.K., and R.G. are employees of EndoCeutics, developer of MS-based steroid assays. A.B. is a consultant of EndoCeutics. mass spectrometry.

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)0.003
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0310.022
Insufficient payload (model declined to judge)0.0040.004

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.

Opus teacher head0.082
GPT teacher head0.374
Teacher spread0.292 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations11
Published2015
Admission routes1
Has abstractno

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