How this could be wrong
Per-hypothesis “what would change this” lists exist on the working model. This page states how thewhole approach can fail. It is not a disclaimer substitute and not a diagnosis of error.
No original laboratory report, DXA printout, radiology report, or clinic note has been reviewed by this project. Nearly every numeric and interpretive claim rests on patient-compiled specialty summaries, an evidence pack, and a video transcript.
1. Transcription error
Specialty summaries and pack tables can mis-copy values, dates, units, or assay names from the underlying reports. Known surface conflicts (for example forearm T-score and lumbar anchors that differ between video and compiled summary) show that the present record is not self-consistent at every cell. If a key number is wrong, hypotheses that depend on it are wrong even when the reasoning is careful.
2. Missing primary records
Without facility DXA printouts and LSC/precision data, longitudinal BMD comparisons across scanners or sites cannot be treated as reliable improvement or decline. Without method and limit-of-detection detail for assays such as KIT, “negative” results cannot be graded. Missing formation markers leave turnover classification open. Absence of a record is not evidence for or against a disease.
3. Shared model scaffolding
Round-1 analyses used independent model families, but they shared the same compiled inputs, claim inventory, and research questions. Agreement under shared scaffolding is not independent clinical validation. Models can be wrong and can agree on errors. Multi-model “agreement” language on this site means cross-checking under shared inputs, not voting and not medical authority.
The Round-2 review (2026-08-10) used a single model family, so its independence is weaker still: the deriving agent was blinded to this project’s conclusions, but blinding cannot surface blind spots shared across one vendor’s models, and the round still read the same compiled summaries as every other analysis. Where that round “independently re-derived” a published position, read it as one model family reaching the same answer twice under shared inputs — worth something, and less than cross-family agreement, which is itself less than clinical review.
4. Literature-summary error
Cards may be abstract-only. Identifier resolution (DOI/PMID) is not proof that a card’s summary accurately represents the paper. Applicability to this case can be overstated. A corrected or mismatched identifier can silently remove the intended support for a claim.
4b. Published errata and post-publication updates
Cited works can later carry corrigenda, expressions of concern, or retractions that this portfolio has not yet reflected. Identity verification does not track the full post-publication amendment record. Known published corrigenda at launch (check the publisher record for any card you rely on):
- lit-0015 — Shuhart et al. 2023 ISCD Adult PDC executive summary (J Clin Densitom) (J Clin Densitom 2025; doi 10.1016/j.jocd.2024.101548, PMID 39706737).
- lit-0057 — IDSA 2020 babesiosis guideline (PMID 33252652) (Clin Infect Dis 2021; doi 10.1093/cid/ciab275, Erratum PMID 33960362).
- lit-0205 — Perez et al. 2017 Specific Antibody Deficiency (PMID 28588580) (Front Immunol 2018; doi 10.3389/fimmu.2018.00450, Erratum PMID 29576764).
5. Anchoring bias
A ranked differential and a polished evidence atlas can narrow a clinician’s search—the same failure mode that can delay recognition of ordinary or modular explanations. That is why the clinician handout leads with gaps and open questions before ranked hypotheses, and why the atlas ends in open questions rather than a diagnosis band.
6. Selection bias
The public corpus was assembled for research packaging. Tests that were ordered, summarized, or highlighted may over-represent certain domains; unperformed or unsummarized work is invisible. Topic literature search was targeted, not an exhaustive systematic review of every specialty.
7. Coexisting common processes
Several common or semi-independent processes may coexist without a single rare unifier. The null model H-NULL on theworking model exists for that baseline honesty. Preferring an elegant stack can under-weight ordinary multi-morbidity.
What still helps
Structured claim types, source-class badges, two-channel specialty testing, open-question registers, and pre-registered qualitative outcomes for clinician questions reduce—but do not eliminate—these risks. A licensed clinician must verify underlying records.
Related surfaces: Methods ·For clinicians ·Prediction / outcome matrix