Structure vs. Chain-of-Thought: Evaluating LLM Criteria Extraction for Depression Severity

Xinkai Chen

cs.CLSep 30, 2026

Structure vs. Chain-of-Thought: Evaluating LLM Criteria Extraction for Depression Severity

Authors: Xinkai Chen

Organizations: Independent Researcher

Abstract

A large language model (LLM) can rate depression severity directly from a social media post or mark which clinical criteria the post shows and let code turn the count into a label. The latter is easier to audit because a clinician can check each marked criterion. We compare these approaches on two Reddit corpora using three LLMs (from 9B to frontier scale) and two questionnaires (PHQ-9, BDI-II), and measure agreement with quadratic weighted kappa. For the two frontier models, criteria extraction scores above chain-of-thought on one corpus only when its decision thresholds are fitted on labeled data. Neither model's gain is significant, with or without recalibrating chain-of-thought on the same labels. With thresholds fixed a priori from PHQ-9's criteria, extraction shows no gain on either corpus, even where models mark over two criteria per post. The 9B model behaves differently on a corpus from depression communities. It labels most posts severe, whether prompted directly or with chain-of-thought, while the a priori rule beats both without labels. After chain-of-thought is recalibrated on the same labels, no significant gap remains, consistent with a calibration effect. Yet higher ordinal agreement does not ensure better detection of severe cases. PHQ-9 criteria extraction misses most severe posts, and moving from direct prompting to chain-of-thought and then to extraction increases misses in nearly all comparisons. On the primary corpus, a relabeled stress dataset, a model using that dataset's own features, including word counts from the text, is not significantly different from frontier criteria extraction under the a priori rule.

Figures & tables

	DepSeverity	DepSign
lowest class	513 minimum	184 not dep.
middle class(es)	58 mild / 79 mod.	472 mod.
highest class	56 severe	50 severe
majority acc.	0.727	0.669
majority $\kappa_{w}$	0.000	0.000
median words	80	104

Table I: Test splits. Both corpora are evaluated at

n{=}706

DepSeverity (4 classes, 56 severe )
Model	Cond.	$\kappa_{w}$	MAE	Acc	F1 M	sev $\downarrow$
Qwen3.5-9B	C1	0.288	0.948	0.445	0.335	29/56
	C2	0.340	0.877	0.456	0.327	34/56
	C3	0.489 / 0.322	0.465	0.728	0.367	43/56
	C4	0.495 /0.470/0.057	0.517	0.643	0.420	40/56
DeepSeek	C1	0.219	1.280	0.329	0.251	16/56

Table II: Test-set results,

n{=}706

per corpus.

\kappa_{w}

= quadratic weighted kappa; F1 M = macro-F1; sev

\downarrow

= fraction of severe posts given a lower label (

1-{}

recall on that class). C3 rows show fitted / a priori (DSM-5) thresholds, C4 rows fitted / DSM-5 / BDI-II bands; other columns are fitted. Bold: highest

\kappa_{w}

per model and corpus, fewest missed severe posts per corpus.

Fig. 1: Paired bootstrap

\Delta\kappa_{w}

on the same posts (4000 resamples); solid markers exclude zero at 95%, faded hollow ones do not. C2

-

C1 uses no thresholds. C4 is in Table II .

Appendix figures & tables8 assets

Supplementary material from the paper’s appendix.

Appendix

Corpus	Model	Comparison	$\Delta\kappa_{w}$ [95% CI]
DepSeverity	Qwen3.5-9B	C2 $-$ C1	+0.052 [+0.009, +0.098]	C
DepSeverity	Qwen3.5-9B	C3 fitted $-$ C2	+0.149 [+0.072, +0.223]	C,H
DepSeverity	Qwen3.5-9B	C3 a priori $-$ C2	$-$ 0.018 [ $-$ 0.089, +0.054]	C
DepSeverity	Qwen3.5-9B	C4 fitted $-$ C2	+0.155 [+0.082, +0.227]
DepSeverity	Qwen3.5-9B	C4 a priori $-$ C2	+0.130 [+0.050, +0.208]
DepSeverity	Qwen3.5-9B	C3 fitted $-$ C1	+0.201 [+0.127, +0.276]

Appendix

Table III: All paired comparisons (

\Delta\kappa_{w}

, 95% paired bootstrap interval, 4000 resamples). Rows marked C are the confirmatory family; H = survives Holm correction at

\alpha=0.05

. C4 uses the DSM-5 rule for a priori .

Corpus	Model	Cond.	fitted	a priori
DepSeverity	Qwen3.5-9B	C3	[0.5, 1, 1.5]	[0.5, 2.5, 4.5]
DepSeverity	Qwen3.5-9B	C4	[0.5, 1.5, 3.5]	[0.5, 2.5, 4.5]
DepSeverity	DeepSeek	C3	[0.5, 1.5, 2.5]	[0.5, 2.5, 4.5]
DepSeverity	DeepSeek	C4	[0.5, 1.5, 3.5]	[0.5, 2.5, 4.5]
DepSeverity	Claude	C3	[0.5, 1.5, 2.5]	[0.5, 2.5, 4.5]
DepSeverity	Claude	C4	[0.5, 2.5, 5.5]	[0.5, 2.5, 4.5]

Appendix

Table IV: Thresholds on the count of present criteria (C3: 0–9, C4: 0–21). Fitted thresholds maximize

\kappa_{w}

on the 600-post fitting split.

Corpus	Model		raw	+cal	map	C3 fitted $-$ +cal
DepSeverity	Qwen3.5-9B	C1	0.288	0.320	0012	+0.169 [+0.090, +0.246]
DepSeverity	Qwen3.5-9B	C2	0.340	0.379	0013	+0.109 [+0.027, +0.189]
DepSeverity	DeepSeek	C1	0.219	0.330	0002	+0.196 [+0.113, +0.277]
DepSeverity	DeepSeek	C2	0.504	0.504	0123	+0.022 [ $-$ 0.039, +0.086]
DepSeverity	Claude	C1	0.299	0.352	0013	+0.147 [+0.067, +0.226]
DepSeverity	Claude	C2	0.462	0.462	0123	+0.037 [ $-$ 0.029, +0.103]

Appendix

Table V: C1 and C2 given the same labels as C3: a monotone relabeling fitted on the 600-post fitting split. The map lists, for each predicted label from lowest to highest, the label it becomes ( 0123 leaves labels unchanged). The last column is C3 fitted minus the recalibrated condition.

Corpus	Model	Comparison	$\Delta\kappa_{w}$ [95% CI]
DepSeverity	Qwen3.5-9B	C3 a priori $-$ C2	$-$ 0.058 [ $-$ 0.136, +0.023]
DepSeverity	Qwen3.5-9B	C4 fitted $-$ C2	+0.116 [+0.024, +0.202]
DepSeverity	Qwen3.5-9B	C4 a priori $-$ C2	+0.091 [ $-$ 0.001, +0.181]
DepSeverity	Qwen3.5-9B	C3 a priori $-$ C1	+0.002 [ $-$ 0.072, +0.079]
DepSeverity	Qwen3.5-9B	C4 fitted $-$ C1	+0.176 [+0.085, +0.259]
DepSeverity	Qwen3.5-9B	C4 a priori $-$ C1	+0.150 [+0.060, +0.235]

Appendix

Table VI: The a priori rules and C4 against C1 and C2. Every C1 and C2 prediction here is recalibrated on the 600-post fitting split with the monotone relabeling of Table V .

\Delta\kappa_{w}

with 95% paired bootstrap interval; exploratory; bold intervals exclude zero.

Model	Cond.	minimum	mild	moderate	severe
Qwen3.5-9B	C1	0.46	0.29	0.43	0.48
	C2	0.50	0.26	0.38	0.39
	C3 fitted	0.92	0.00	0.35	0.23
	C3 a priori	0.92	0.36	0.03	0.00
	C4 fitted	0.77	0.29	0.35	0.29
	C4 a priori	0.77	0.53	0.14	0.12

Appendix

Table VII: Per-class recall on DepSeverity test (

n

per class: 513, 58, 79, 56).

Model	Cond.	not dep.	moderate	severe
Qwen3.5-9B	C1	0.22	0.24	0.74
	C2	0.13	0.16	0.80
	C3 fitted	0.40	0.85	0.04
	C3 a priori	0.40	0.78	0.06
	C4 fitted	0.38	0.70	0.24
	C4 a priori	0.38	0.52	0.42

Appendix

Table VIII: Per-class recall on DepSign test (

n

per class: 184, 472, 50).

Corpus	Model	C1	C2	C3	C4
DepSeverity	Qwen3.5-9B	179 / 3	212 / 519	535 / 186	767 / 459
DepSeverity	DeepSeek	162 / 2	194 / 363	499 / 152	712 / 371
DepSeverity	Claude	238 / 5	282 / 545	737 / 227	1021 / 541
DepSign	Qwen3.5-9B	276 / 3	309 / 492	633 / 200	865 / 476
DepSign	DeepSeek	262 / 2	294 / 429	600 / 171	813 / 404
DepSign	Claude	369 / 4	413 / 614	871 / 251	1155 / 579

Appendix

Table IX: Mean tokens per test call, input / output, as reported by each provider.

Variant	pres./post	absent	fitted	a pr.	vs C2, fitted	vs C2, a pr.
original	0.45	0.30%	0.526	0.423	+0.022 [ $-$ 0.039, +0.086]	$-$ 0.081 [ $-$ 0.147, $-$ 0.010]
permissive	0.72	0.25%	0.507	0.425	+0.002 [ $-$ 0.059, +0.067]	$-$ 0.079 [ $-$ 0.145, $-$ 0.012]
symmetric evidence	0.47	0.35%	0.555	0.433	+0.051 [ $-$ 0.011, +0.116]	$-$ 0.071 [ $-$ 0.138, +0.000]

Appendix

Table X: C3 prompt variants, DeepSeek-V4.1-Flash on DepSeverity (same nine criteria and JSON schema; wording only). Last two columns:

\Delta\kappa_{w}

against C2.

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